In this post I explore different tools and methods that can be used to try and interpret "black box" models.

Since the NFL draft is today, I thought a fun dataset to use for this topic would be the NFL combine data. Using that data we'll first build a model to predict the early career performance for NFL defensive ends (DE). After that, we will use different interpretability methods to better understand the model.

Creating a model

Lets get started by importing what we need to set up the data and our modeling pipeline.

In [1]:
%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd

from sklearn.preprocessing import Imputer
from sklearn.model_selection import  cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.metrics import make_scorer
from sklearn.ensemble import RandomForestRegressor

from skll.metrics import spearman

from skopt import BayesSearchCV
from skopt.space import Real, Categorical, Integer

import warnings
In [2]:
# setting up the styling for the plots in this notebook
sns.set(style="white", palette="colorblind", font_scale=1.2, 
        rc={"figure.figsize":(12,9)})
RANDOM_STATE = 420
N_JOBS=8

Here we load in the data that contains each player's combine information and their 3 year Approximate Value, which we will use as measure of early career performance (and going forward will be abbreviated as AV). When building our model we won't be predicting a defensive end's AV, instead we'll be predicting be where his AV ranks (as a percentile) among other defensive ends. Players with high a AV should be closer to 1, while players with low AV should be closer to 0.

NOTE: If you want read up on which combine measurables matter for each NFL position, take a look at Bill Lotter's series of blog posts on the topic.

In [3]:
# read in our data
data_df = pd.read_csv('data/combine_data_since_2000_PROCESSED_2018-04-26.csv')
# onyl get players that have been in the league for 3 years
data_df2 = data_df.loc[data_df.Year <= 2015].copy()
# calculate the player AV percentiles by position
data_df2['AV_pctile'] = (data_df2.groupby('Pos')
                                  .AV
                                  .rank(pct=True,
                                        method='min', 
                                        ascending=True))

Now that we've loaded the data and calculated the AV percentiles, lets get the DE data and create a training set and testing set. We will train and tune our model on the first 8 years (2000-2011) of combine data and then test it on the next 4 years (2012-2015).

In [4]:
# Get the data for the position we want, in this case it's DE
pos_df = data_df2.loc[data_df2.Pos=='DE'].copy().reset_index(drop=True)

# Split the data into train and test sets
train_df = pos_df.loc[pos_df.Year <= 2011]
test_df = pos_df.loc[pos_df.Year.isin([2012, 2013, 2014, 2015])]

We'll be build a random forest model since that seems to be the most common "black box" algorithm people use at work. One thing to to note is that in our modeling Pipeline we will need to include an Imputer because some DEs are missing data as they did not participate in all the combine drills.

In [5]:
# Combine measurables
features = ['Forty',
            'Wt',
            'Ht',
            'Vertical',
            'BenchReps',
            'BroadJump',
            'Cone',
            'Shuttle']
# what we want to predict
target = 'AV_pctile'

X = train_df[features].values
y = train_df[target].values

# the modeling pipeline
pipe = Pipeline([("imputer", Imputer()),
                 ("estimator", RandomForestRegressor(random_state=RANDOM_STATE))])

To tune our model we will use BayesSearchCV from scikit-optimize, which utilizes bayesian optimization to find the best hyperparameters. We'll use Spearman's rank correlation as our scoring metric since we are mainly concerned with the ranking of players when it comes to the draft.

In [6]:
# We use spearman's rank correlation as the scoring metric since
# we are concerned with ranking the players
spearman_scorer = make_scorer(spearman)

# the hyperparamters to search over, including different imputation strategies
rf_param_space = {
    'imputer__strategy': Categorical(['mean', 'median', 'most_frequent']),
    'estimator__max_features': Integer(1, 8),
    'estimator__n_estimators': Integer(50, 500), 
    'estimator__min_samples_split': Integer(2, 200),
}
# create our search object
search = BayesSearchCV(pipe, 
                      rf_param_space, 
                      cv=10,
                      n_jobs=N_JOBS, 
                      verbose=0, 
                      error_score=-9999, 
                      scoring=spearman_scorer, 
                      random_state=RANDOM_STATE,
                      return_train_score=True, 
                      n_iter=75)
# fit the model
# I get some funky warnings, possibly due to the spearman scorer,
# I choose to suppress them
with warnings.catch_warnings():
    warnings.filterwarnings('ignore')
    search.fit(X, y) 
In [7]:
# best model parameters
search.best_params_
Out[7]:
{'estimator__max_features': 6,
 'estimator__min_samples_split': 63,
 'estimator__n_estimators': 500,
 'imputer__strategy': 'median'}
In [8]:
# CV score
search.best_score_
Out[8]:
0.38699012444314973
In [9]:
# CV standard deviation
search.cv_results_['std_test_score'][search.best_index_]
Out[9]:
0.13872941403084682

Now that we've tuned our model lets evaluate it on the test set.

In [10]:
# test set data
X_test = test_df[features].values
y_test = test_df[target].values
# predictions
y_pred = search.predict(X_test)
# evaluation
model_test_score = spearman_scorer(search, X_test, y_test)
model_test_score
Out[10]:
0.20929440372118885

So our model's predictions had a Spearman's rank correlation of about 0.209. Is that good or bad? I'm not sure. To get a better sense of how good or how bad of a score that is we can use the actual draft picks as a comparison point.

In [11]:
# create percentiles for nfl draft picks
# Lower numerical picks (e.g. 1, 2, 3) are ranked closer to 1
# Higher numerical picks (e.g. 180, 200, etc) are ranked closer to 0
draft_pick_pctile = test_df.Pick.rank(pct=True,
                                      method='min', 
                                      ascending=False, 
                                      na_option='top')
spearman(y_test, draft_pick_pctile)
Out[11]:
0.6425823905799751

It looks like NFL teams are 3 times better at ranking DEs than our model.

Ok great, now that we have our model lets explore the different tools we can use to better understand it.

Feature Importance

Mean Decrease Impurity

When using a tree-ensemble like random forest you can find out which features the model found valuable by checking the feature importances. In scikit-learn the feature importances are a reflection of how well a feature reduces some criterion. In our regression example that criterion is mean squared error. This method for calculating feature importance is typically called mean decrease impurity or gini importance.

We can access our model's feature importances with the feature_importances_ attribute.

In [12]:
# get the estimator and imputer from our pipeline, which will be used
# as we try and interpret our model
estimator = search.best_estimator_.named_steps['estimator']
imputer = search.best_estimator_.named_steps['imputer']

estimator.feature_importances_
Out[12]:
array([0.34196584, 0.3615381 , 0.01465109, 0.03990757, 0.05417351,
       0.05790136, 0.07712715, 0.05273537])

Alternatively, we could use eli5's explain_weights_df function, which returns the importances and the feature names we pass it as a pandas DataFrame.

In [13]:
import eli5

# create our dataframe of feature importances
feat_imp_df = eli5.explain_weights_df(estimator, feature_names=features)
feat_imp_df
Out[13]:
feature weight std
0 Wt 0.361538 0.111257
1 Forty 0.341966 0.129695
2 Cone 0.077127 0.106589
3 BroadJump 0.057901 0.088515
4 BenchReps 0.054174 0.081691
5 Shuttle 0.052735 0.079622
6 Vertical 0.039908 0.077301
7 Ht 0.014651 0.037999

So it looks like a player's weight and their forty time are important to model. A thing to note is that explain_weights_df also returns the standard deviations, but they may not be trustworthy as those values assume a normal distribution. Instead of relying on those standard deviations we can access each tree in our ensemble and plot the full distribution of feature importances.

In [14]:
# get the feature importances from each tree and then visualize the
# distributions as boxplots
all_feat_imp_df = pd.DataFrame(data=[tree.feature_importances_ for tree in 
                                     estimator],
                               columns=features)

(sns.boxplot(data=all_feat_imp_df)
        .set(title='Feature Importance Distributions',
             ylabel='Importance'));

Permutation Importance

Permutation importances or mean decrease accuracy (MDA) is an alternative to mean decrease impurity that can be applied to any model. The basic idea of permutation importance is to permute the values of each feature and measure how much that permutation negatively impacts the scoring metric (which in our case is the Spearman's rank correlation). This gives us a sense of how our model would perform without that specific feature. All we need to do calculate permutation importance is use PermutationImportance from eli5.

In [15]:
from eli5.sklearn import PermutationImportance

# we need to impute the data first before calculating permutation importance
train_X_imp = imputer.transform(X)
# set up the met-estimator to calculate permutation importance on our training
# data
perm_train = PermutationImportance(estimator, scoring=spearman_scorer,
                                   n_iter=50, random_state=RANDOM_STATE)
# fit and see the permuation importances
perm_train.fit(train_X_imp, y)
eli5.explain_weights_df(perm_train, feature_names=features)
Out[15]:
feature weight std
0 Wt 0.317768 0.038552
1 Forty 0.261409 0.036946
2 Cone 0.033025 0.006485
3 Shuttle 0.023978 0.005698
4 BroadJump 0.017353 0.005326
5 BenchReps 0.016859 0.004783
6 Vertical 0.012590 0.003537
7 Ht 0.005083 0.001254
In [16]:
# plot the distributions
perm_train_feat_imp_df = pd.DataFrame(data=perm_train.results_,
                                      columns=features)
(sns.boxplot(data=perm_train_feat_imp_df)
        .set(title='Permutation Importance Distributions (training data)',
             ylabel='Importance'));

Based on the permutation importances it again looks like Forty and Wt are the two most important features to the model.

Feature Contributions

While feature importances can provide us insight into which variables a model finds valuable, they don't tell us how those features impact our model's predictions. One way to actually do that is by using the decision paths in our trees to see how much each feature changes the predictions when going from the parent to the child node. In the end we can break down these feature contributions in a linear manner such that our predictions can be interpreted like so:

$$prediction = bias + feature_{1}contribution + feature_{2}contribution +... feature_{n}contribution$$

Ando Saabas has written a few blog posts that delve into this topic. For now I will try to explain how feature contributions are calculated by looking at an example that uses a shallow tree in our forest.

In [17]:
from IPython.display import Image  
from sklearn.tree import export_graphviz
import graphviz

# Get all trees of depth 2 in the random forest
depths2 = [tree for tree in estimator.estimators_ if tree.tree_.max_depth==2]
# grab the first one
tree = depths2[0]
# now plot the full tree
dot_data = export_graphviz(tree, out_file=None, feature_names=features, 
                           filled=True, rounded=True, special_characters=True)
graph = graphviz.Source(dot_data)  
graph
Out[17]:
Tree 0 <path d="M265,-261C265,-261 183,-261 183,-261 177,-261 171,-255 171,-249 171,-249 171,-205 171,-205 171,-199 177,-193 183,-193 183,-193 265,-193 265,-193 271,-193 277,-199 277,-205 277,-205 277,-249 277,-249 277,-255 271,-261 265,-261" fill="#e58139" fill-opacity="0.501961" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="190.5" y="-245.8">Wt ≤ 270.5</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="186" y="-230.8">mse = 0.128</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="179" y="-215.8">samples = 204</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="183" y="-200.8">value = 0.414</text> </g> <!-- 1 --> <g class="node" id="node2"><title>1 <path d="M205,-157C205,-157 123,-157 123,-157 117,-157 111,-151 111,-145 111,-145 111,-101 111,-101 111,-95 117,-89 123,-89 123,-89 205,-89 205,-89 211,-89 217,-95 217,-101 217,-101 217,-145 217,-145 217,-151 211,-157 205,-157" fill="#e58139" fill-opacity="0.298039" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="123.5" y="-141.8">Forty ≤ 4.755</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="126" y="-126.8">mse = 0.126</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="119" y="-111.8">samples = 118</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="123" y="-96.8">value = 0.317</text> </g> <!-- 0->1 --> <g class="edge" id="edge1"><title>0->1 <path d="M204.52,-192.884C199.49,-184.332 194.008,-175.013 188.748,-166.072" fill="none" stroke="black"></path> <polygon fill="black" points="191.675,-164.144 183.588,-157.299 185.641,-167.693 191.675,-164.144" stroke="black"></polygon> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="middle" x="177.314" y="-177.799">True</text> </g> <!-- 4 --> <g class="node" id="node5"><title>4 <path d="M321,-157C321,-157 247,-157 247,-157 241,-157 235,-151 235,-145 235,-145 235,-101 235,-101 235,-95 241,-89 247,-89 247,-89 321,-89 321,-89 327,-89 333,-95 333,-101 333,-101 333,-145 333,-145 333,-151 327,-157 321,-157" fill="#e58139" fill-opacity="0.807843" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="243.5" y="-141.8">Forty ≤ 4.885</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="246" y="-126.8">mse = 0.094</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="243" y="-111.8">samples = 86</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="246.5" y="-96.8">value = 0.56</text> </g> <!-- 0->4 --> <g class="edge" id="edge4"><title>0->4 <path d="M243.48,-192.884C248.51,-184.332 253.992,-175.013 259.252,-166.072" fill="none" stroke="black"></path> <polygon fill="black" points="262.359,-167.693 264.412,-157.299 256.325,-164.144 262.359,-167.693" stroke="black"></polygon> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="middle" x="270.686" y="-177.799">False</text> </g> <!-- 2 --> <g class="node" id="node3"><title>2 <path d="M86,-53C86,-53 12,-53 12,-53 6,-53 7.10543e-15,-47 7.10543e-15,-41 7.10543e-15,-41 7.10543e-15,-12 7.10543e-15,-12 7.10543e-15,-6 6,-0 12,-0 12,-0 86,-0 86,-0 92,-0 98,-6 98,-12 98,-12 98,-41 98,-41 98,-47 92,-53 86,-53" fill="#e58139" fill-opacity="0.643137" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="11" y="-37.8">mse = 0.141</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="8" y="-22.8">samples = 57</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="8" y="-7.8">value = 0.481</text> </g> <!-- 1->2 --> <g class="edge" id="edge2"><title>1->2 <path d="M123.779,-88.9485C112.201,-79.4346 99.5927,-69.074 88.0848,-59.6175" fill="none" stroke="black"></path> <polygon fill="black" points="90.268,-56.8814 80.3198,-53.2367 85.8238,-62.2897 90.268,-56.8814" stroke="black"></polygon> </g> <!-- 3 --> <g class="node" id="node4"><title>3 <path d="M202,-53C202,-53 128,-53 128,-53 122,-53 116,-47 116,-41 116,-41 116,-12 116,-12 116,-6 122,-0 128,-0 128,-0 202,-0 202,-0 208,-0 214,-6 214,-12 214,-12 214,-41 214,-41 214,-47 208,-53 202,-53" fill="none" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="127" y="-37.8">mse = 0.071</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="124" y="-22.8">samples = 61</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="124" y="-7.8">value = 0.175</text> </g> <!-- 1->3 --> <g class="edge" id="edge3"><title>1->3 <path d="M164.35,-88.9485C164.437,-80.7153 164.531,-71.848 164.619,-63.4814" fill="none" stroke="black"></path> <polygon fill="black" points="168.122,-63.2732 164.728,-53.2367 161.122,-63.1991 168.122,-63.2732" stroke="black"></polygon> </g> <!-- 5 --> <g class="node" id="node6"><title>5 <path d="M320,-53C320,-53 246,-53 246,-53 240,-53 234,-47 234,-41 234,-41 234,-12 234,-12 234,-6 240,-0 246,-0 246,-0 320,-0 320,-0 326,-0 332,-6 332,-12 332,-12 332,-41 332,-41 332,-47 326,-53 320,-53" fill="#e58139" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="245" y="-37.8">mse = 0.075</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="242" y="-22.8">samples = 47</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="242" y="-7.8">value = 0.651</text> </g> <!-- 4->5 --> <g class="edge" id="edge5"><title>4->5 <path d="M283.65,-88.9485C283.563,-80.7153 283.469,-71.848 283.381,-63.4814" fill="none" stroke="black"></path> <polygon fill="black" points="286.878,-63.1991 283.272,-53.2367 279.878,-63.2732 286.878,-63.1991" stroke="black"></polygon> </g> <!-- 6 --> <g class="node" id="node7"><title>6 <path d="M436,-53C436,-53 362,-53 362,-53 356,-53 350,-47 350,-41 350,-41 350,-12 350,-12 350,-6 356,-0 362,-0 362,-0 436,-0 436,-0 442,-0 448,-6 448,-12 448,-12 448,-41 448,-41 448,-47 442,-53 436,-53" fill="#e58139" fill-opacity="0.552941" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="361" y="-37.8">mse = 0.093</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="358" y="-22.8">samples = 39</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="358" y="-7.8">value = 0.438</text> </g> <!-- 4->6 --> <g class="edge" id="edge6"><title>4->6 <path d="M324.221,-88.9485C335.799,-79.4346 348.407,-69.074 359.915,-59.6175" fill="none" stroke="black"></path> <polygon fill="black" points="362.176,-62.2897 367.68,-53.2367 357.732,-56.8814 362.176,-62.2897" stroke="black"></polygon> </g> </g> </svg> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>To keep things simple lets find the feature contributions for a player that follows the leftmost path in the tree above, say a player who weighs 260 lbs and runs 4.6 seconds in the forty yard dash.</p> <p>The player first starts at the root (top) node, where the mean AV percentile of all the samples is 0.414 (this is value of our bias term). At the first split, the tree considers whether the player is 270.5 lbs or less, so any change in the prediction caused by this split is attributed to the player's weight. Our player falls into the left child node because he weighs 260 lbs. Since the average percentile at this node is 0.317 versus 0.414 in the parent node, we can say that the player's weight caused a decrease of 0.097 percentage points in his predicted AV percentile. Now for the split at this new node, the tree considers whether the player runs a 4.755 forty or less. The player in our example runs a 4.6 forty, so after this split, he ends up in the leftmost leaf node of the tree. At this leaf node the player's final predicted percentile is 0.481, since that is an increase of 0.164 percentage points from the previous node, we can say that the player's forty time contributed 0.164 percentage points to his predicted AV percentile. In the end, the feature contributions for this player's predicted AV percentile are as follows:</p> $$\underset{\text{AV %ile}}{0.481} = \underset{\text{bias}}{0.414}-\underset{\text{Wt}}{0.097}+\underset{\text{Forty}}{0.164}$$<p>Which are the contribution values we get when we use <code>eli5</code>'s <code>explain_prediction_df</code> function.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [18]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># simple exmaple of a player with a 4.6 Forty and a Wt of 260 lbs</span> <span class="n">example</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">4.6</span><span class="p">,</span> <span class="mi">260</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="n">eli5</span><span class="o">.</span><span class="n">explain_prediction_df</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="n">example</span><span class="p">,</span> <span class="n">feature_names</span><span class="o">=</span><span class="n">features</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[18]:</div> <div class="output_html rendered_html output_subarea output_execute_result"> <div> <style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>target</th> <th>feature</th> <th>weight</th> <th>value</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>y</td> <td><bias></td> <td>0.413820</td> <td>1.0</td> </tr> <tr> <th>1</th> <td>y</td> <td>Forty</td> <td>0.163935</td> <td>4.6</td> </tr> <tr> <th>2</th> <td>y</td> <td>Wt</td> <td>-0.097034</td> <td>260.0</td> </tr> </tbody> </table> </div> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>And 0.481 should be what the tree predicts.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [19]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">example</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[19]:</div> <div class="output_text output_subarea output_execute_result"> <pre>array([0.48072043])</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Which it does. To calculate the bias term and feature contributions for the entire forest of trees, all you do is average the bias terms and feature contributions of all the trees.</p> <p><strong>NOTE:</strong> I'm aware of two libraries that allow you to easily calculate these kinds of feature contributions, <code>eli5</code> and Ando Sabaas' <code>treeintepretter</code>. Besides for the difference in their APIs there are differences in which algorithms they can be used with. <code>treeinterpretter</code> currently works for the following <code>scikit-learn</code> estimators:</p> <ul> <li>DecisionTreeRegressor</li> <li>DecisionTreeClassifier</li> <li>ExtraTreeRegressor</li> <li>ExtraTreeClassifier</li> <li>RandomForestRegressor</li> <li>RandomForestClassifier</li> <li>ExtraTreesRegressor</li> <li>ExtraTreesClassifier</li> </ul> <p><code>eli5</code> works for those estimator plus both of <code>scikit-learn</code>'s gradient boosting estimators as well as XGBoost and LightGBM estimators.</p> <p>Now lets actually get the feature contributions for each sample in our training and testing sets. One thing to note is that the <code>explain_prediction_df</code> only calculates the contributions one observation at a time, which can be time consuming. To speed things up I wrote a helper function that lets us use multiple processes (i.e. multiple cpu cores) to get the feature contributions for all our predictions.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [20]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">concurrent.futures</span> <span class="k">import</span> <span class="n">ProcessPoolExecutor</span> <span class="k">def</span> <span class="nf">multiproc_iter_func</span><span class="p">(</span><span class="n">max_workers</span><span class="p">,</span> <span class="n">an_iter</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">item_kwarg</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="sd">"""</span> <span class="sd"> A helper functions that applies a function to each item in an iterable using</span> <span class="sd"> multiple processes. 'item_kwarg' is the keyword argument for the item in the</span> <span class="sd"> iterable that we pass to the function.</span> <span class="sd"> """</span> <span class="k">with</span> <span class="n">ProcessPoolExecutor</span><span class="p">(</span><span class="n">max_workers</span><span class="o">=</span><span class="n">max_workers</span><span class="p">)</span> <span class="k">as</span> <span class="n">executor</span><span class="p">:</span> <span class="n">future_results</span> <span class="o">=</span> <span class="p">[</span><span class="n">executor</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span><span class="n">item_kwarg</span><span class="p">:</span> <span class="n">item</span><span class="p">},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">an_iter</span><span class="p">]</span> <span class="n">results</span> <span class="o">=</span> <span class="p">[</span><span class="n">future</span><span class="o">.</span><span class="n">result</span><span class="p">()</span> <span class="k">for</span> <span class="n">future</span> <span class="ow">in</span> <span class="n">future_results</span><span class="p">]</span> <span class="k">return</span> <span class="n">results</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [21]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Contibrutions for training set predictions</span> <span class="c1"># construct a list of all contributions for the entire train set</span> <span class="n">train_expl_list</span> <span class="o">=</span> <span class="n">multiproc_iter_func</span><span class="p">(</span><span class="n">N_JOBS</span><span class="p">,</span> <span class="n">train_X_imp</span><span class="p">,</span> <span class="n">eli5</span><span class="o">.</span><span class="n">explain_prediction_df</span><span class="p">,</span> <span class="s1">'doc'</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span> <span class="n">feature_names</span><span class="o">=</span><span class="n">features</span><span class="p">)</span> <span class="c1"># concatenate them into 1 large dataframe, with the proper player name as an</span> <span class="c1"># index</span> <span class="n">train_expl_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">train_expl_list</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="n">train_df</span><span class="o">.</span><span class="n">Player</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'Player'</span><span class="p">])</span> <span class="c1"># take a look at a couple of players</span> <span class="n">train_expl_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">18</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[21]:</div> <div class="output_html rendered_html output_subarea output_execute_result"> <div> <style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th></th> <th>target</th> <th>feature</th> <th>weight</th> <th>value</th> </tr> <tr> <th>Player</th> <th></th> <th></th> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <th rowspan="9" valign="top">Michael Boireau</th> <th>0</th> <td>y</td> <td><bias></td> <td>0.421925</td> <td>1.000</td> </tr> <tr> <th>1</th> <td>y</td> <td>Wt</td> <td>0.050163</td> <td>274.000</td> </tr> <tr> <th>2</th> <td>y</td> <td>BenchReps</td> <td>0.005912</td> <td>26.000</td> </tr> <tr> <th>3</th> <td>y</td> <td>Ht</td> <td>-0.000552</td> <td>76.000</td> </tr> <tr> <th>4</th> <td>y</td> <td>Shuttle</td> <td>-0.003833</td> <td>4.490</td> </tr> <tr> <th>5</th> <td>y</td> <td>Vertical</td> <td>-0.004394</td> <td>29.000</td> </tr> <tr> <th>6</th> <td>y</td> <td>BroadJump</td> <td>-0.008272</td> <td>105.000</td> </tr> <tr> <th>7</th> <td>y</td> <td>Cone</td> <td>-0.010553</td> <td>7.680</td> </tr> <tr> <th>8</th> <td>y</td> <td>Forty</td> <td>-0.103266</td> <td>5.090</td> </tr> <tr> <th rowspan="9" valign="top">Courtney Brown</th> <th>0</th> <td>y</td> <td><bias></td> <td>0.421925</td> <td>1.000</td> </tr> <tr> <th>1</th> <td>y</td> <td>Forty</td> <td>0.020799</td> <td>4.780</td> </tr> <tr> <th>2</th> <td>y</td> <td>BenchReps</td> <td>0.010175</td> <td>24.000</td> </tr> <tr> <th>3</th> <td>y</td> <td>Shuttle</td> <td>0.005032</td> <td>4.410</td> </tr> <tr> <th>4</th> <td>y</td> <td>Cone</td> <td>-0.000513</td> <td>7.365</td> </tr> <tr> <th>5</th> <td>y</td> <td>BroadJump</td> <td>-0.003199</td> <td>114.000</td> </tr> <tr> <th>6</th> <td>y</td> <td>Ht</td> <td>-0.003440</td> <td>77.000</td> </tr> <tr> <th>7</th> <td>y</td> <td>Vertical</td> <td>-0.004461</td> <td>33.000</td> </tr> <tr> <th>8</th> <td>y</td> <td>Wt</td> <td>-0.046967</td> <td>269.000</td> </tr> </tbody> </table> </div> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [22]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Contributions for test set predictions</span> <span class="c1"># we need to impute the missing values in the test set</span> <span class="n">test_X_imp</span> <span class="o">=</span> <span class="n">imputer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="c1"># now repeat what we did with the training data on the test data</span> <span class="n">test_expl_list</span> <span class="o">=</span> <span class="n">multiproc_iter_func</span><span class="p">(</span><span class="n">N_JOBS</span><span class="p">,</span> <span class="n">test_X_imp</span><span class="p">,</span> <span class="n">eli5</span><span class="o">.</span><span class="n">explain_prediction_df</span><span class="p">,</span> <span class="s1">'doc'</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span> <span class="n">feature_names</span><span class="o">=</span><span class="n">features</span><span class="p">)</span> <span class="n">test_expl_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">test_expl_list</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="n">test_df</span><span class="o">.</span><span class="n">Player</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'Player'</span><span class="p">])</span> <span class="n">test_expl_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">18</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[22]:</div> <div class="output_html rendered_html output_subarea output_execute_result"> <div> <style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th></th> <th>target</th> <th>feature</th> <th>weight</th> <th>value</th> </tr> <tr> <th>Player</th> <th></th> <th></th> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <th rowspan="9" valign="top">Frank Alexander</th> <th>0</th> <td>y</td> <td><bias></td> <td>0.421925</td> <td>1.000</td> </tr> <tr> <th>1</th> <td>y</td> <td>Forty</td> <td>0.025510</td> <td>4.800</td> </tr> <tr> <th>2</th> <td>y</td> <td>BenchReps</td> <td>0.011177</td> <td>24.000</td> </tr> <tr> <th>3</th> <td>y</td> <td>Shuttle</td> <td>0.001877</td> <td>4.410</td> </tr> <tr> <th>4</th> <td>y</td> <td>Wt</td> <td>0.001765</td> <td>270.000</td> </tr> <tr> <th>5</th> <td>y</td> <td>Cone</td> <td>0.000624</td> <td>7.365</td> </tr> <tr> <th>6</th> <td>y</td> <td>BroadJump</td> <td>-0.001727</td> <td>114.000</td> </tr> <tr> <th>7</th> <td>y</td> <td>Ht</td> <td>-0.002257</td> <td>76.000</td> </tr> <tr> <th>8</th> <td>y</td> <td>Vertical</td> <td>-0.004324</td> <td>33.000</td> </tr> <tr> <th rowspan="9" valign="top">Jake Bequette</th> <th>0</th> <td>y</td> <td><bias></td> <td>0.421925</td> <td>1.000</td> </tr> <tr> <th>1</th> <td>y</td> <td>Wt</td> <td>0.063637</td> <td>274.000</td> </tr> <tr> <th>2</th> <td>y</td> <td>Forty</td> <td>0.041246</td> <td>4.750</td> </tr> <tr> <th>3</th> <td>y</td> <td>Cone</td> <td>0.035639</td> <td>6.900</td> </tr> <tr> <th>4</th> <td>y</td> <td>Shuttle</td> <td>0.015801</td> <td>4.070</td> </tr> <tr> <th>5</th> <td>y</td> <td>BenchReps</td> <td>0.010608</td> <td>24.000</td> </tr> <tr> <th>6</th> <td>y</td> <td>Vertical</td> <td>0.001309</td> <td>34.000</td> </tr> <tr> <th>7</th> <td>y</td> <td>Ht</td> <td>-0.001039</td> <td>77.000</td> </tr> <tr> <th>8</th> <td>y</td> <td>BroadJump</td> <td>-0.001561</td> <td>113.000</td> </tr> </tbody> </table> </div> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [23]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Double check that the sums of contributions equal the actual predictions</span> <span class="n">y_pred_sums</span> <span class="o">=</span> <span class="n">test_expl_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Player'</span><span class="p">)</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_pred_sums</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[23]:</div> <div class="output_text output_subarea output_execute_result"> <pre>True</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h2 id="Plotting-feature-contributions">Plotting feature contributions<a class="anchor-link" href="#Plotting-feature-contributions">¶</a></h2><p>Now that we have all these feature contributions lets make some plots to better understand them.</p> <h3 id="Boxplots">Boxplots<a class="anchor-link" href="#Boxplots">¶</a></h3><p>First lets make a boxplot to see the contribution distributions of each feature.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [24]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># I"m creating one big dataframe that includes both train and test</span> <span class="c1"># to plot them on same plot using seaborn's boxplot</span> <span class="n">train_expl_df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">'weight'</span><span class="p">:</span> <span class="s1">'contribution'</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">test_expl_df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">'weight'</span><span class="p">:</span> <span class="s1">'contribution'</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">train_expl_df</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'train'</span> <span class="n">test_expl_df</span><span class="p">[</span><span class="s1">'data'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'test'</span> <span class="n">train_test_expl_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">train_expl_df</span><span class="p">,</span> <span class="n">test_expl_df</span><span class="p">])</span> <span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'feature'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">features</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train_test_expl_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">train_test_expl_df</span><span class="o">.</span><span class="n">feature</span><span class="o">!=</span><span class="s1">'<bias>'</span><span class="p">],</span> <span class="n">palette</span><span class="o">=</span><span class="p">{</span><span class="s1">'train'</span><span class="p">:</span> <span class="s1">'salmon'</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">:</span><span class="s1">'deepskyblue'</span><span class="p">})</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Distributions of Feature Contributions'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h3 id="Swarmplots">Swarmplots<a class="anchor-link" href="#Swarmplots">¶</a></h3><p>We can also use swarmplots which allow for a more granular view of the distribution since they plot each individual observation. If we color each point by the associated feature's (scaled) value, we can view how the change in a feature's value changes along with its contribution.</p> <p><code>seaborn</code> doesn't support a colorbar by default so it's something we have to add on our own. I hacked together function (based on <a href="https://stackoverflow.com/questions/40814612/map-data-points-to-colormap-with-seaborn-swarmplot">this</a> stackoverflow answer) that will let us add a vertical colorbar to the right of our swarmplots.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [25]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colorbar</span> <span class="k">import</span> <span class="n">ColorbarBase</span> <span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1</span> <span class="k">import</span> <span class="n">make_axes_locatable</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [26]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># a hacky function that plots a swarmplot along with a colorbar</span> <span class="c1"># based off the code found here:</span> <span class="c1"># https://stackoverflow.com/questions/40814612/map-data-points-to-colormap-with-seaborn-swarmplot</span> <span class="k">def</span> <span class="nf">swarmplot_with_cbar</span><span class="p">(</span><span class="n">cmap</span><span class="p">,</span> <span class="n">cbar_label</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">swarmplot</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># remove the legend, because we want to set a colorbar instead</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span> <span class="c1">## create colorbar ##</span> <span class="n">divider</span> <span class="o">=</span> <span class="n">make_axes_locatable</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span> <span class="n">ax_cb</span> <span class="o">=</span> <span class="n">divider</span><span class="o">.</span><span class="n">new_horizontal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="s2">"3%"</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">(</span><span class="n">ax_cb</span><span class="p">)</span> <span class="n">cb</span> <span class="o">=</span> <span class="n">ColorbarBase</span><span class="p">(</span><span class="n">ax_cb</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">'vertical'</span><span class="p">)</span> <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="n">cbar_label</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="k">return</span> <span class="n">fig</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [27]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># min-max scaling of the feature values allows us to use a colorbar</span> <span class="c1"># to indicate high or low feature values</span> <span class="n">train_scaled_feat_vals</span> <span class="o">=</span> <span class="p">(</span><span class="n">train_expl_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'feature'</span><span class="p">)</span> <span class="o">.</span><span class="n">value</span> <span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">/</span><span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span> <span class="n">train_expl_df</span><span class="p">[</span><span class="s1">'scaled_feat_vals'</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_scaled_feat_vals</span> <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'viridis'</span><span class="p">)</span> <span class="n">cbar_label</span> <span class="o">=</span> <span class="s1">'Feature Value </span><span class="si">%i</span><span class="s1">le'</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Distribution of Feature Contributions (training data)'</span><span class="p">)</span> <span class="n">swarmplot_with_cbar</span><span class="p">(</span><span class="n">cmap</span><span class="p">,</span> <span class="n">cbar_label</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'feature'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">'scaled_feat_vals'</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">features</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train_expl_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">train_expl_df</span><span class="o">.</span><span class="n">feature</span><span class="o">!=</span><span class="s1">'<bias>'</span><span class="p">]);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [28]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_scaled_feat_vals</span> <span class="o">=</span> <span class="p">(</span><span class="n">test_expl_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'feature'</span><span class="p">)</span> <span class="o">.</span><span class="n">value</span> <span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">/</span><span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span> <span class="n">test_expl_df</span><span class="p">[</span><span class="s1">'scaled_feat_vals'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_scaled_feat_vals</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Distribution of Feature Contributions (test data)'</span><span class="p">)</span> <span class="n">swarmplot_with_cbar</span><span class="p">(</span><span class="n">cmap</span><span class="p">,</span> <span class="n">cbar_label</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'feature'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">'scaled_feat_vals'</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">features</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">test_expl_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">test_expl_df</span><span class="o">.</span><span class="n">feature</span><span class="o">!=</span><span class="s1">'<bias>'</span><span class="p">]);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Based on both plots, we can see that faster forty times and higher weights result in more positive contributions. The other features tend to have most of their contributions hover around 0. It's also interest to note the gaps in middle of the Wt distributions on both plots.</p> <h3 id="Plotting-Feature-Contributions-against-Feature-Values">Plotting Feature Contributions against Feature Values<a class="anchor-link" href="#Plotting-Feature-Contributions-against-Feature-Values">¶</a></h3><p>Lets plot the feature contributions against the feature values to get a better sense of how they relate to one another. We can use <code>seaborn</code>'s <code>lmplot</code> to easily create a grid of these kinds of plots for both our training and testing data.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [29]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fg</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">lmplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">col</span><span class="o">=</span><span class="s1">'feature'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train_expl_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">train_expl_df</span><span class="o">.</span><span class="n">feature</span><span class="o">!=</span><span class="s1">'<bias>'</span><span class="p">],</span> <span class="n">col_order</span><span class="o">=</span><span class="n">features</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">col_wrap</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">fit_reg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span><span class="s1">'salmon'</span><span class="p">,</span> <span class="s1">'alpha'</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span> <span class="s1">'s'</span><span class="p">:</span><span class="mi">30</span><span class="p">})</span> <span class="n">fg</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Feature Contributions vs Feature Values (training data)'</span><span class="p">)</span> <span class="n">fg</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.90</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [30]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fg</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">lmplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'value'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">col</span><span class="o">=</span><span class="s1">'feature'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">test_expl_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">test_expl_df</span><span class="o">.</span><span class="n">feature</span><span class="o">!=</span><span class="s1">'<bias>'</span><span class="p">],</span> <span class="n">col_order</span><span class="o">=</span><span class="n">features</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">col_wrap</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">fit_reg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">scatter_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span><span class="s1">'salmon'</span><span class="p">,</span> <span class="s1">'alpha'</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span> <span class="s1">'s'</span><span class="p">:</span><span class="mi">30</span><span class="p">})</span> <span class="n">fg</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Feature Contributions vs Feature Values (testing data)'</span><span class="p">)</span> <span class="n">fg</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.90</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>In both plots for Wt it's interesting to see the rapid increase in contribution at around 270 lbs. The model essentially believes that weighing more than 270 is automatically a positive factor for a player, while weighing less than that is a negative one.</p> <p>One thing to note about these plots is that when we see different contributions (e.g. -0.05, -0.10, -0.15) for the same feature value (e.g. a forty time of 5 seconds) there is probably another feature (or set of features) that is causing these differences. To view such feature interactions we can set the color of the dots to reflect the value of another feature. Lets take a look at how a player's weight interacts with the contribution of their forty time (at least in the training set).</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [31]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># before we actually plot anything we need to do a bit of data manipulation</span> <span class="c1"># lets pivot the data and create a new dataframe where the columns are</span> <span class="c1"># the feature contributions and each row is a player, with the player</span> <span class="c1"># name as the index value</span> <span class="c1"># here are different ways to pivot column values to columns</span> <span class="c1"># https://stackoverflow.com/questions/26255671/pandas-column-values-to-columns</span> <span class="c1"># based on running %%timeit, the groupby method was fastest </span> <span class="n">train_contrib_df</span> <span class="o">=</span> <span class="p">(</span><span class="n">train_expl_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Player'</span><span class="p">,</span><span class="s1">'feature'</span><span class="p">])</span> <span class="o">.</span><span class="n">contribution</span> <span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="s1">'first'</span><span class="p">)</span> <span class="o">.</span><span class="n">unstack</span><span class="p">())</span> <span class="c1"># add in the feature values</span> <span class="n">train_feat_contrib_df</span> <span class="o">=</span> <span class="n">train_contrib_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">train_df</span><span class="p">[[</span><span class="s1">'Player'</span><span class="p">]</span> <span class="o">+</span> <span class="n">features</span><span class="p">],</span> <span class="n">how</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">left_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">'Player'</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="s1">'_contrib'</span><span class="p">,</span> <span class="s1">'_value'</span><span class="p">))</span> <span class="c1"># now we can plot</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'Forty_value'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'Forty_contrib'</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'Wt_value'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train_feat_contrib_df</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Forty'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'contribution'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>We can see that there is some interaction between weight and forty time. Given a specific forty time, players with higher weights tend to have a more positive (or less negative) contribution.</p> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h3 id="Heatmaps">Heatmaps<a class="anchor-link" href="#Heatmaps">¶</a></h3><p>With a little data wrangling and <code>seaborn</code>'s <code>heatmap</code> function we can take a look at the full set of contributions for each player in our test set.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [32]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">double_heatmap</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="n">cbar_label1</span><span class="p">,</span> <span class="n">cbar_label2</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">subplot_top</span><span class="o">=</span><span class="mf">0.86</span><span class="p">,</span> <span class="n">cmap1</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="n">cmap2</span><span class="o">=</span><span class="s1">'magma'</span><span class="p">,</span> <span class="n">center1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">center2</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">grid_height_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">10</span><span class="p">)):</span> <span class="c1"># do the actual plotting</span> <span class="c1"># here we plot 2 seperate heatmaps one for the predictions and actual percentiles</span> <span class="c1"># the other for the contributions</span> <span class="c1"># the reason I chose to do this is because of the difference in magnitudes</span> <span class="c1"># between the percentiles and the contributions</span> <span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span><span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">'height_ratios'</span><span class="p">:</span><span class="n">grid_height_ratios</span><span class="p">})</span> <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="n">subplot_top</span><span class="p">)</span> <span class="c1"># heatmap for actual and predicted percentiles</span> <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"viridis"</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">xticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="n">center1</span><span class="p">,</span> <span class="n">cbar_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'location'</span><span class="p">:</span><span class="s1">'top'</span><span class="p">,</span> <span class="s1">'use_gridspec'</span><span class="p">:</span><span class="kc">False</span><span class="p">,</span> <span class="s1">'pad'</span><span class="p">:</span><span class="mf">0.1</span><span class="p">,</span> <span class="s1">'label'</span><span class="p">:</span> <span class="n">cbar_label1</span><span class="p">})</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span> <span class="c1"># heatmap of the feature contributions</span> <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">xticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="n">center2</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap2</span><span class="p">,</span> <span class="n">cbar_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'location'</span><span class="p">:</span><span class="s1">'bottom'</span><span class="p">,</span> <span class="s1">'use_gridspec'</span><span class="p">:</span><span class="kc">False</span><span class="p">,</span> <span class="s1">'pad'</span><span class="p">:</span><span class="mf">0.07</span><span class="p">,</span> <span class="s1">'shrink'</span><span class="p">:</span><span class="mf">0.41</span><span class="p">,</span> <span class="s1">'label'</span><span class="p">:</span> <span class="n">cbar_label2</span><span class="p">})</span> <span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">''</span><span class="p">);</span> <span class="k">return</span> <span class="n">fig</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [33]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get the prediction and actual target values to plot</span> <span class="n">y_test_and_pred_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)),</span> <span class="n">index</span><span class="o">=</span><span class="n">test_df</span><span class="o">.</span><span class="n">Player</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'true_AV_pctile'</span><span class="p">,</span> <span class="s1">'pred_AV_pctile'</span><span class="p">])</span> <span class="c1"># lets pivot the data such that the feature contributions are the columns</span> <span class="n">test_heatmap_df</span> <span class="o">=</span> <span class="p">(</span><span class="n">test_expl_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Player'</span><span class="p">,</span><span class="s1">'feature'</span><span class="p">])</span> <span class="o">.</span><span class="n">contribution</span> <span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="s1">'first'</span><span class="p">)</span> <span class="o">.</span><span class="n">unstack</span><span class="p">())</span> <span class="c1"># there may be some NaNs if a feature did not contribute to a prediction, </span> <span class="c1"># so fill them in with 0s</span> <span class="n">test_heatmap_df</span> <span class="o">=</span> <span class="n">test_heatmap_df</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># merge our predictions with the the contributions</span> <span class="n">test_heatmap_df</span> <span class="o">=</span> <span class="n">test_heatmap_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">y_test_and_pred_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">left_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># sort by predictions</span> <span class="n">test_heatmap_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'pred_AV_pctile'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [34]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">title</span> <span class="o">=</span> <span class="s1">'Feature contributions to predicted AV </span><span class="si">%i</span><span class="s1">le </span><span class="se">\n</span><span class="s1">for each player in the testing data'</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">double_heatmap</span><span class="p">(</span><span class="n">test_heatmap_df</span><span class="p">[[</span><span class="s1">'true_AV_pctile'</span><span class="p">,</span> <span class="s1">'pred_AV_pctile'</span><span class="p">]]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">test_heatmap_df</span><span class="p">[</span><span class="n">features</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="s1">'</span><span class="si">%i</span><span class="s1">le'</span><span class="p">,</span> <span class="s1">'contribution'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>The plot above consist of two heatmaps. Each column in both heatmaps represents a player. The rows in the top heatmap represent the true and predicted AV percentiles while the rows in the bottom heatmap represent the the feature contributions for each player prediction.</p> <p>I like the above visualization because it makes it easy to view a large set of predictions and contributions all at once. I definitely prefer it over the alternative of viewing each set of contributions through a printout out of the <code>DataFrame</code>.</p> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h1 id="Joint-Feature-Contributions">Joint Feature Contributions<a class="anchor-link" href="#Joint-Feature-Contributions">¶</a></h1><p>A benefit of using a tree-ensemble like our random forest model, is that it captures interactions among our features without us explicitly defining them. However in our attempt to interpret our model we have only looked at the importances and contributions of individual features. Since we've yet to measure the impact of the feature interactions that the model has found, we have an incomplete picture of what our model is actually doing. To gain some insight into these feature interactions we can use the <code>treeinterpeter</code> package. It uses the same method as before to calculate contributions, but instead of crediting individual features along the decision paths, <code>treeinterpeter</code> allows us to credit the feature interactions.</p> <p><strong>NOTE:</strong> If you use XGBoost you can use the <a href="https://github.com/limexp/xgbfir">xgbfir</a> package to inspect feature interactions.</p> <p>Lets use <code>treeinterpreter</code> on our simple decision tree from before, in order to get an idea of of how joint feature contributions (i.e. the contributions of the feature interactions) are calculated.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [35]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># A reminder of what our tree looks like</span> <span class="n">graph</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[35]:</div> <div class="output_svg output_subarea output_execute_result"> <!-- Generated by graphviz version 2.38.0 (20140413.2041) --> <!-- Title: Tree Pages: 1 --> <svg height="269pt" viewbox="0.00 0.00 456.00 269.00" width="456pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g class="graph" id="graph0" transform="scale(1 1) rotate(0) translate(4 265)"> <title>Tree <polygon fill="white" points="-4,4 -4,-265 452,-265 452,4 -4,4" stroke="none"></polygon> <!-- 0 --> <g class="node" id="node1"><title>0 <path d="M265,-261C265,-261 183,-261 183,-261 177,-261 171,-255 171,-249 171,-249 171,-205 171,-205 171,-199 177,-193 183,-193 183,-193 265,-193 265,-193 271,-193 277,-199 277,-205 277,-205 277,-249 277,-249 277,-255 271,-261 265,-261" fill="#e58139" fill-opacity="0.501961" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="190.5" y="-245.8">Wt ≤ 270.5</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="186" y="-230.8">mse = 0.128</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="179" y="-215.8">samples = 204</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="183" y="-200.8">value = 0.414</text> </g> <!-- 1 --> <g class="node" id="node2"><title>1 <path d="M205,-157C205,-157 123,-157 123,-157 117,-157 111,-151 111,-145 111,-145 111,-101 111,-101 111,-95 117,-89 123,-89 123,-89 205,-89 205,-89 211,-89 217,-95 217,-101 217,-101 217,-145 217,-145 217,-151 211,-157 205,-157" fill="#e58139" fill-opacity="0.298039" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="123.5" y="-141.8">Forty ≤ 4.755</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="126" y="-126.8">mse = 0.126</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="119" y="-111.8">samples = 118</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="123" y="-96.8">value = 0.317</text> </g> <!-- 0->1 --> <g class="edge" id="edge1"><title>0->1 <path d="M204.52,-192.884C199.49,-184.332 194.008,-175.013 188.748,-166.072" fill="none" stroke="black"></path> <polygon fill="black" points="191.675,-164.144 183.588,-157.299 185.641,-167.693 191.675,-164.144" stroke="black"></polygon> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="middle" x="177.314" y="-177.799">True</text> </g> <!-- 4 --> <g class="node" id="node5"><title>4 <path d="M321,-157C321,-157 247,-157 247,-157 241,-157 235,-151 235,-145 235,-145 235,-101 235,-101 235,-95 241,-89 247,-89 247,-89 321,-89 321,-89 327,-89 333,-95 333,-101 333,-101 333,-145 333,-145 333,-151 327,-157 321,-157" fill="#e58139" fill-opacity="0.807843" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="243.5" y="-141.8">Forty ≤ 4.885</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="246" y="-126.8">mse = 0.094</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="243" y="-111.8">samples = 86</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="246.5" y="-96.8">value = 0.56</text> </g> <!-- 0->4 --> <g class="edge" id="edge4"><title>0->4 <path d="M243.48,-192.884C248.51,-184.332 253.992,-175.013 259.252,-166.072" fill="none" stroke="black"></path> <polygon fill="black" points="262.359,-167.693 264.412,-157.299 256.325,-164.144 262.359,-167.693" stroke="black"></polygon> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="middle" x="270.686" y="-177.799">False</text> </g> <!-- 2 --> <g class="node" id="node3"><title>2 <path d="M86,-53C86,-53 12,-53 12,-53 6,-53 7.10543e-15,-47 7.10543e-15,-41 7.10543e-15,-41 7.10543e-15,-12 7.10543e-15,-12 7.10543e-15,-6 6,-0 12,-0 12,-0 86,-0 86,-0 92,-0 98,-6 98,-12 98,-12 98,-41 98,-41 98,-47 92,-53 86,-53" fill="#e58139" fill-opacity="0.643137" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="11" y="-37.8">mse = 0.141</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="8" y="-22.8">samples = 57</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="8" y="-7.8">value = 0.481</text> </g> <!-- 1->2 --> <g class="edge" id="edge2"><title>1->2 <path d="M123.779,-88.9485C112.201,-79.4346 99.5927,-69.074 88.0848,-59.6175" fill="none" stroke="black"></path> <polygon fill="black" points="90.268,-56.8814 80.3198,-53.2367 85.8238,-62.2897 90.268,-56.8814" stroke="black"></polygon> </g> <!-- 3 --> <g class="node" id="node4"><title>3 <path d="M202,-53C202,-53 128,-53 128,-53 122,-53 116,-47 116,-41 116,-41 116,-12 116,-12 116,-6 122,-0 128,-0 128,-0 202,-0 202,-0 208,-0 214,-6 214,-12 214,-12 214,-41 214,-41 214,-47 208,-53 202,-53" fill="none" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="127" y="-37.8">mse = 0.071</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="124" y="-22.8">samples = 61</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="124" y="-7.8">value = 0.175</text> </g> <!-- 1->3 --> <g class="edge" id="edge3"><title>1->3 <path d="M164.35,-88.9485C164.437,-80.7153 164.531,-71.848 164.619,-63.4814" fill="none" stroke="black"></path> <polygon fill="black" points="168.122,-63.2732 164.728,-53.2367 161.122,-63.1991 168.122,-63.2732" stroke="black"></polygon> </g> <!-- 5 --> <g class="node" id="node6"><title>5 <path d="M320,-53C320,-53 246,-53 246,-53 240,-53 234,-47 234,-41 234,-41 234,-12 234,-12 234,-6 240,-0 246,-0 246,-0 320,-0 320,-0 326,-0 332,-6 332,-12 332,-12 332,-41 332,-41 332,-47 326,-53 320,-53" fill="#e58139" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="245" y="-37.8">mse = 0.075</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="242" y="-22.8">samples = 47</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="242" y="-7.8">value = 0.651</text> </g> <!-- 4->5 --> <g class="edge" id="edge5"><title>4->5 <path d="M283.65,-88.9485C283.563,-80.7153 283.469,-71.848 283.381,-63.4814" fill="none" stroke="black"></path> <polygon fill="black" points="286.878,-63.1991 283.272,-53.2367 279.878,-63.2732 286.878,-63.1991" stroke="black"></polygon> </g> <!-- 6 --> <g class="node" id="node7"><title>6 <path d="M436,-53C436,-53 362,-53 362,-53 356,-53 350,-47 350,-41 350,-41 350,-12 350,-12 350,-6 356,-0 362,-0 362,-0 436,-0 436,-0 442,-0 448,-6 448,-12 448,-12 448,-41 448,-41 448,-47 442,-53 436,-53" fill="#e58139" fill-opacity="0.552941" stroke="black"></path> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="361" y="-37.8">mse = 0.093</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="358" y="-22.8">samples = 39</text> <text font-family="Helvetica,sans-Serif" font-size="14.00" text-anchor="start" x="358" y="-7.8">value = 0.438</text> </g> <!-- 4->6 --> <g class="edge" id="edge6"><title>4->6 <path d="M324.221,-88.9485C335.799,-79.4346 348.407,-69.074 359.915,-59.6175" fill="none" stroke="black"></path> <polygon fill="black" points="362.176,-62.2897 367.68,-53.2367 357.732,-56.8814 362.176,-62.2897" stroke="black"></polygon> </g> </g> </svg> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>To get the joint feature contributions, we pass in our estimator and the data to the <code>predict</code> function and set <code>joint_contribution</code> to <code>True</code>. That should return the prediction, the bias term and the joint feature contributions for our player.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [36]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">treeinterpreter.treeinterpreter</span> <span class="k">as</span> <span class="nn">ti</span> <span class="c1"># get the contributions for our simple player example</span> <span class="c1"># who has a 4.6 forty and weighs 260 lbs</span> <span class="c1"># joint_contribution=True gets the joint feature contributions</span> <span class="c1"># when set to False it just returns the individual feature contributions</span> <span class="n">example_pred</span><span class="p">,</span> <span class="n">example_bias</span><span class="p">,</span> <span class="n">example_contrib</span> <span class="o">=</span> <span class="n">ti</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="n">example</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">joint_contribution</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [37]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># same prediction as before</span> <span class="n">example_pred</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[37]:</div> <div class="output_text output_subarea output_execute_result"> <pre>array([0.48072043])</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [38]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># same bias value as before</span> <span class="n">example_bias</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[38]:</div> <div class="output_text output_subarea output_execute_result"> <pre>array([0.41381959])</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [39]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">example_contrib</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[39]:</div> <div class="output_text output_subarea output_execute_result"> <pre>[{(0, 1): 0.163934535699354, (1,): -0.09703369274852375}]</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>The joint contributions are returned as list of dictionaries (in our example one dictionary for our one player), where the keys are numeric tuples representing the features (0 represents Forty and 1 represent Wt) and the values are the joint feature contributions. The joint feature contributions for our simple example are as follows:</p> $$\underset{\text{AV %ile}}{0.481} = \underset{\text{bias}}{0.414}-\underset{\text{Wt}}{0.097}+\underset{\text{Forty & Wt}}{0.164}$$<p>The contributions are the same values as before, the difference we see here is that instead of crediting the contribution of 0.164 percentage points to just the player's forty time, we also credit the previous feature, Wt, in the decision path. Remember, we are now crediting feature interactions, not just individual features. We only credit a single feature when it's either at the root node (like Wt is) or if it's the only feature used along a decision path.</p> <p>Lets get the joint feature contributions for the test set predictions.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [40]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">joint_pred</span><span class="p">,</span> <span class="n">joint_bias</span><span class="p">,</span> <span class="n">joint_contrib</span> <span class="o">=</span> <span class="n">ti</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">test_X_imp</span><span class="p">,</span> <span class="n">joint_contribution</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [41]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># double check predictions are correct</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">joint_pred</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[41]:</div> <div class="output_text output_subarea output_execute_result"> <pre>True</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [42]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the bias is still the same</span> <span class="n">joint_bias</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[42]:</div> <div class="output_text output_subarea output_execute_result"> <pre>array([0.42192522, 0.42192522, 0.42192522])</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [43]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 96 observations in test set</span> <span class="nb">len</span><span class="p">(</span><span class="n">joint_contrib</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[43]:</div> <div class="output_text output_subarea output_execute_result"> <pre>96</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [44]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># tuples representing the column indexes of our features</span> <span class="nb">list</span><span class="p">(</span><span class="n">joint_contrib</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())[:</span><span class="mi">3</span><span class="p">]</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[44]:</div> <div class="output_text output_subarea output_execute_result"> <pre>[(0, 1), (0, 1, 2, 5), (0, 1, 5)]</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [45]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># an example of a joint feature contribution</span> <span class="n">joint_contrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[45]:</div> <div class="output_text output_subarea output_execute_result"> <pre>0.01733594064297664</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>To get the joint feature contributions into more useable format lets match the tuples of indexes to the proper feature names. Then we can construct a <code>DataFrame</code> of each player's joint feature contributions, ordered by the absolute value of the contributions.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [46]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_ordered_joint_contrib_df</span><span class="p">(</span><span class="n">contrib</span><span class="p">):</span> <span class="sd">"""</span> <span class="sd"> Creates a dataframe from the joint contribution info, where the</span> <span class="sd"> feature combinations are ordered (in descending fashion) by the absolute</span> <span class="sd"> value of the joint contribution.</span> <span class="sd"> """</span> <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">contrib</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'feat_interaction'</span><span class="p">,</span> <span class="s1">'contribution'</span><span class="p">])</span> <span class="c1"># get the reordered index </span> <span class="n">new_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">contribution</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> <span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">new_idx</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">return</span> <span class="n">df</span> <span class="c1"># add the names of the feats to the joint contributions</span> <span class="n">joint_contrib_w_feat_names</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># for each observation in the join contributions</span> <span class="k">for</span> <span class="n">obs</span> <span class="ow">in</span> <span class="n">joint_contrib</span><span class="p">:</span> <span class="c1"># create a list</span> <span class="n">obs_contrib</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># for each tuple of column indexes</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">obs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span> <span class="c1"># get the associated feature names</span> <span class="n">feature_combo</span> <span class="o">=</span> <span class="p">[</span><span class="n">features</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">k</span><span class="p">]</span> <span class="c1"># get the contribution value</span> <span class="n">contrib</span> <span class="o">=</span> <span class="n">obs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="c1"># store that information in the observation individual list</span> <span class="n">obs_contrib</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">feature_combo</span><span class="p">,</span> <span class="n">contrib</span><span class="p">])</span> <span class="c1"># append that individual to the large list containing each observations</span> <span class="c1"># joint feature contributions</span> <span class="n">joint_contrib_w_feat_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">obs_contrib</span><span class="p">)</span> <span class="c1"># create an ordered dataframe for each player</span> <span class="n">joint_contrib_dfs</span> <span class="o">=</span> <span class="p">[</span><span class="n">create_ordered_joint_contrib_df</span><span class="p">(</span><span class="n">contrib</span><span class="p">)</span> <span class="k">for</span> <span class="n">contrib</span> <span class="ow">in</span> <span class="n">joint_contrib_w_feat_names</span><span class="p">]</span> <span class="c1"># now combine them all</span> <span class="n">joint_contrib_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">joint_contrib_dfs</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="n">test_df</span><span class="o">.</span><span class="n">Player</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'Player'</span><span class="p">])</span> <span class="c1"># edit feat_interaction column so the values are strings and not lists</span> <span class="n">joint_contrib_df</span><span class="p">[</span><span class="s1">'feat_interaction'</span><span class="p">]</span> <span class="o">=</span> <span class="n">joint_contrib_df</span><span class="o">.</span><span class="n">feat_interaction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="s1">' | '</span><span class="o">.</span><span class="n">join</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [47]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">joint_contrib_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[47]:</div> <div class="output_html rendered_html output_subarea output_execute_result"> <div> <style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th></th> <th>feat_interaction</th> <th>contribution</th> </tr> <tr> <th>Player</th> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <th rowspan="5" valign="top">Frank Alexander</th> <th>0</th> <td>Forty | Wt</td> <td>0.017336</td> </tr> <tr> <th>1</th> <td>Forty</td> <td>0.010549</td> </tr> <tr> <th>2</th> <td>Wt</td> <td>-0.005477</td> </tr> <tr> <th>3</th> <td>Forty | Wt | BenchReps</td> <td>0.003433</td> </tr> <tr> <th>4</th> <td>Forty | Wt | Shuttle</td> <td>0.002922</td> </tr> </tbody> </table> </div> </div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Great, now we have the joint feature contributions for each player in our test set in a nice <code>DataFrame</code>. Lets take a look at how important each feature and feature interaction is to our predictions. To do that we will measure (as a percentage) how much of the total joint contributions an individual feature or feature interaction is responsible for. In other words we are going to measure the relative importance of each feature and feature interaction.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [48]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># first get the sum of the absolute values for each joint feature contribution</span> <span class="n">abs_imp_joint_contrib</span> <span class="o">=</span> <span class="p">(</span><span class="n">joint_contrib_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'feat_interaction'</span><span class="p">)</span> <span class="o">.</span><span class="n">contribution</span> <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> <span class="c1"># then calculate the % of total contribution by dividing by the sum of all absolute vals</span> <span class="n">rel_imp_join_contrib</span> <span class="o">=</span> <span class="n">abs_imp_joint_contrib</span> <span class="o">/</span> <span class="n">abs_imp_joint_contrib</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="n">rel_imp_join_contrib</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'barh'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'salmon'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Joint Feature Importances'</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Features'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'</span><span class="si">% o</span><span class="s1">f total joint contributions'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img src="data:image/png;base64,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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>It looks like the interaction between a Forty and Wt was the most important feature interaction in our predictions, accounting for over 20% of the joint feature contributions. Overall Forty, Wt and their interaction account for more than 50% of the total feature contributions.</p> <p>We can also take a look at the distributions of contributions for each feature and feature interaction.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [49]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">top_feat_interactions</span> <span class="o">=</span> <span class="n">rel_imp_join_contrib</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span><span class="o">.</span><span class="n">index</span> <span class="n">top_contrib_mask</span> <span class="o">=</span> <span class="n">joint_contrib_df</span><span class="o">.</span><span class="n">feat_interaction</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">top_feat_interactions</span><span class="p">)</span> <span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s1">'feat_interaction'</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">joint_contrib_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">top_contrib_mask</span><span class="p">],</span> <span class="n">orient</span><span class="o">=</span><span class="s1">'h'</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">top_feat_interactions</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>And finally lets make another double heatmap plot to observe some of the joint contributions for each prediction in our test set.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [50]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">joint_contrib_heatmap_df</span> <span class="o">=</span> <span class="p">(</span><span class="n">joint_contrib_df</span><span class="p">[</span><span class="n">top_contrib_mask</span><span class="p">]</span> <span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Player'</span><span class="p">,</span><span class="s1">'feat_interaction'</span><span class="p">])</span> <span class="o">.</span><span class="n">contribution</span> <span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="s1">'first'</span><span class="p">)</span> <span class="o">.</span><span class="n">unstack</span><span class="p">())</span> <span class="n">joint_contrib_heatmap_df</span> <span class="o">=</span> <span class="n">joint_contrib_heatmap_df</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="n">joint_contrib_heatmap_df</span> <span class="o">=</span> <span class="n">joint_contrib_heatmap_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">y_test_and_pred_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">left_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># sort by predictions</span> <span class="n">joint_contrib_heatmap_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'pred_AV_pctile'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">title</span> <span class="o">=</span> <span class="s1">'Top 15 Joint Feature Contributions to predicted AV </span><span class="si">%i</span><span class="s1">le</span><span class="se">\n</span><span class="s1">(testing data)'</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">double_heatmap</span><span class="p">(</span><span class="n">joint_contrib_heatmap_df</span><span class="p">[[</span><span class="s1">'true_AV_pctile'</span><span class="p">,</span> <span class="s1">'pred_AV_pctile'</span><span class="p">]]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">joint_contrib_heatmap_df</span><span class="p">[</span><span class="n">top_feat_interactions</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">cbar_label1</span><span class="o">=</span><span class="s1">'</span><span class="si">%i</span><span class="s1">le'</span><span class="p">,</span> <span class="n">cbar_label2</span><span class="o">=</span><span class="s1">'contribution'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">grid_height_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">subplot_top</span><span class="o">=</span><span class="mf">0.89</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h1 id="PDP-and-ICE-plots">PDP and ICE plots<a class="anchor-link" href="#PDP-and-ICE-plots">¶</a></h1><p>Individual Conditional Expectation (ICE) plots allow us to visualize how changes for a given feature impact the predictions for a set of observations.</p> <p>Lets use <code>pycebox</code> to create an ICE plot to view how changes in the forty yards dash impact our prediction in our training data.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [51]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pycebox.ice</span> <span class="k">import</span> <span class="n">ice</span><span class="p">,</span> <span class="n">ice_plot</span> <span class="c1"># pcyebox likes the data to be in a DataFrame so lets create one with our imputed data</span> <span class="c1"># we first need to impute the missing data</span> <span class="n">train_X_imp_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train_X_imp</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">features</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>We get the ICE values for our feature of interest (Forty) using the <code>ice</code> function.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [52]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">forty_ice_df</span> <span class="o">=</span> <span class="n">ice</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_X_imp_df</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="s1">'Forty'</span><span class="p">,</span> <span class="n">predict</span><span class="o">=</span><span class="n">search</span><span class="o">.</span><span class="n">predict</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>And then we create the ICE plot with the <code>ice_plot</code> function.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [53]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">ice_plot</span><span class="p">(</span><span class="n">forty_ice_df</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'dimgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Pred. AV </span><span class="si">%i</span><span class="s1">le'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Forty'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>So how was the above plot created and what is it telling us? To create an ICE plot, we first pick a feature of interest. Then for each observation we make predictions across a range of values for that feature, while holding all other features constant. Finally we just visualize those predictions as curves on a plot. By plotting these curves we are able to observe the relationship between the feature of interest and the predicted target variable.</p> <p>In our ICE plot above we can see how each player's predicted AV percentile tends to decrease in a non-linear manner between forty times of 4.6 seconds and 5.0 seconds. We can also see that each player's prediction is impacted in a different manner. For example, it looks like the player predictions at the top of the plot do not decrease as much as those at the bottom. The differences we see among the curves indicate that there are interactions between the forty times and the other features.</p> <p>To inspect feature interactions we can color the ICE curves by another feature. We can do that by passing in a feature to <code>ice_plot</code>'s <code>color_by</code> parameter. Lets color each line by the player's weight.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [54]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># new colormap for ICE plot</span> <span class="n">cmap2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'OrRd'</span><span class="p">)</span> <span class="c1"># set color_by to Wt, in order to color each curve by that player's weight</span> <span class="n">ice_plot</span><span class="p">(</span><span class="n">forty_ice_df</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color_by</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap2</span><span class="p">)</span> <span class="c1"># ice_plot doesn't return a colorbar so we have to add one</span> <span class="c1"># hack to add in colorbar taken from here:</span> <span class="c1"># https://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots/11558629#11558629</span> <span class="n">wt_vals</span> <span class="o">=</span> <span class="n">forty_ice_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="s1">'Wt'</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="n">sm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="n">cmap2</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="n">wt_vals</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">vmax</span><span class="o">=</span><span class="n">wt_vals</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span> <span class="c1"># need to create fake array for the scalar mappable or else we get an error</span> <span class="n">sm</span><span class="o">.</span><span class="n">_A</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Pred. AV </span><span class="si">%i</span><span class="s1">le'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Forty'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>From the plot above we can see that heavier players are not impacted in the same manner as lighter ones.</p> <p>We can also add the PDP by setting the <code>plot_pdp</code> to <code>True</code> in the <code>ice_plot</code> function. To adjust the styling of the PDP line we pass a dictionary of settings to <code>pdp_kwargs</code>.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [55]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">ice_plot</span><span class="p">(</span><span class="n">forty_ice_df</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">color_by</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap2</span><span class="p">,</span> <span class="n">plot_pdp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pdp_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">'c'</span><span class="p">:</span> <span class="s1">'k'</span><span class="p">,</span> <span class="s1">'linewidth'</span><span class="p">:</span> <span class="mi">5</span><span class="p">})</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Pred. AV </span><span class="si">%i</span><span class="s1">le'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Forty'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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wva1kp1KlMMA1dxGa7ySoyS8vVmbhXDRebZlxCvWkTTn+8leNypqLZNfNE8zMZ6FBTQdYySMspPPp7Y8y8y+eHHOXxNM/nX3YG6iTzX3kpRVcquvpbG999j4U034C0vx8jOITB0OEZWNorLRWjuHOpffZmiS8fgq+wPgGfAYApvGUfzi8/S8dkn5Jxz8VZL/5AkSZJ6TubsbhkZ7PZA5ulnY4XDG24QAmwLkUxiJ+IbdlBLJDbopiYSCexkAhLmTx+YEL2jg5oQKIaOkVeI0HTiSxYS+vwTRKrqQObZF3dVOXBXDCDnmtsJffwuaiCA/8DRaDl56+Xuuir6M8TvJ/zHh5jx8efsHXiEvKtu2lFXt8PlHnMsOUceRXLNGpLNTSRbWojVrCbZ3EyioQHbFtQ8/0+MrEyKL/8tqtuNouvknHcp8WVLqP39eDJPPWOna64iSZIkbT833HAD06ZNIxKJkJubyyWXXMLpp58OwJdffsn48eOpra1l+PDh3H///RQVFQGQSCQYO3YsH3zwAV6vl0suuYQLL7xwu4xZBrs9oDUtRW2p3XBDZ7ApBKga6Lpzc+somgs0H2gGpMqTrXvrLFmGmrrXjbWPO/fpDYHsZrITcRLLlxFfupj48mpEMgE4zSaanniQtGNOwjvMWdSlaBrBo0/c6LncFQMI7L0/e40N8N9b7mBxYT7u994g/diTt8u17IwUTcOVk4MrJ2eDbXY8zqq/PU2kqopFN11P/mn/R+YhhwLgLq+k8La7aPn3C84s7wVjUD3e7Tx6SZKkn7edYWZ3zJgx3HvvvbhcLpYuXcp5553H4MGD6dOnD1dccQUTJkxg9OjRPPzww1x77bW8+uqrADz66KMsX76cKVOm0NTUxHnnnUe/fv04+OCDt9EVrSWD3R6I2QFMO7P7jarmfMWbSlNAVVAUFUXtnHRVUGwFLAF21CmVZVlgm+s8TiKsdX62TGf7j+ktM7uA7vVi6BqGTxAYUth1XfHWKGtqVhOe/iXRWdPJ/NVFm7Vwyr//IZh1qzno3gl8eNOt+A2VgX1L0PMKejau7FwUvXf/z0V1uyn5zRUkmppY8eTj1Lz0Ik0fvEfptdfjys1DUVWyzzyXxOpV1D14H4H9DsTo5n0UQmA2N5FcvRKzuWnDF+rB36pIJlBcbnIv+jWqVwbXkiRJO1r//v27HiuKgqIorFixgrlz59K/f3+OPfZYAK688kr23Xdfli5dSr9+/Zg0aRL33Xcf6enppKenc/rppzNx4kQZ7O5smqdOJVZd1f1GIVIBqwW27dxb6zy2LbBshLC3+ricvN/eEOwKLFTsQAYE0lHcboIDBhAcPBhfkUqelqB5fi16XiENf7qbrHMvxSgsWv8MlkVixTLii+bhG7U/enYOaaeehfnXhzng2mv59KGHcHs8uH9QhQCc99FbXo4rJ2f92XQBZn0NwrTwDByCf/+D0YIbHi9Mk9iCubS8+ToN779P5qGH0fe2sei72AyoKyeHyjt+R3jRQlY++QTzr/gNBWf8kvxf/J+zvagvhbeOJ/zNlyQb6ro9h56di2/oCLSs7J/8zUSyroa6h+4j/biT8O8+6iedS5IkaVe3M/y//bhx45g4cSKxWIwhQ4ZwyCGH8NBDDzFw4MCufXw+HyUlJSxZsoScnBwaGhoYNGhQ1/ZBgwYxefLk7TJeGez2QPGY3+zoIWxACNHVUGGXJwTJ+jriC+aQWLYEOx4nsWg2a+bPYnUkCYaLPv0zUFyCpKrT+vpLuPqWgKKQXL3SOYeqYvQpBsOg+ek/k3fzXSiKQtbFV2A/cj8jjj2GaS+/htZNG18BaH4/uQMHUrz7cHJG7UX6gQfjLihMDU8QXziPNf/+F3YohJ6bi6u0gtjc77AiYdrnzqNt/kLaEiYNrW0EJk5i1Ssv40lLo/Ciyyi86NJuy6TtrPwDBjLowYdpmzGD5Q/9iVV/f4YhTzyFt6QURVEI7L3/dhmHUdCHwlvHs+b1VwhP+5ycCy9Hdf/8FhpKkiTtLMaNG8edd97Jt99+y9dff43L5SISiZCVlbXefoFAgHA4TCQSASAYDHZtCwaDhLtbB7UNyGB3F6coipPn20u4+pY4AWyKsG2Sq1cQ+vg98AdY0x6n4/3JpBVmYXnS0ItK0HwBSM/CbKwHwFqxHFdBPomWNbROfJnM085C0Q2yLrkSRfkLwx96pNvXNiNhVj71BCvfeYdZ384k+bdnMTLSKRg2jMIhg/GlpWHk5uEpqcRbXILmchFbPJ+OmnqaPvuM1atX09rSRrrPy4jjj6duxgxW1IQRZjvL77uH7D/cSyAznezjT6bomhtwZW+YN7szSt9zT4b/60Xav5vFnIsuwFtaRv8J9+IuLPzRY4UQRJdXs2bKFNq+/opEU1PPpiUEpO+7H+7CQhRdx9a9LLrkfAL7H0Tuqf+Hu6Bn6SiSJEm7sp0hZ7eTpmmMGjWKN998k5deegmfz0coFFpvn3A4jN/vx+fzARAKhXCnJn1CoRD+HjRj+ilksCvt1BRVxVVcRtaFvyY6azrmJ+9T/OBjtE2fxuqn/0LDnAW4MjLQ/W5UO762XfMyD1nFeYQ+m4J/5D64yirQ0jJI+8XZtP/nOVR/oNvXyxtUTt6gKwCI1tVT9/En1E37km+n/JeEbaPoGrrHTVpGOhmBALZlsqquESuRpGTgQA55+lkyDzwIgCFAvKaG1f98ltr/fkpdUxM10Sjac8+R/fw/SAsGyT72BPpefzPu/O6DNmHbmA11JFYuJ7FyObGqxUQWLyJaXU28sQkrFkfYljMtvQ5P3yJGTJ66VX4HndKG785eH0+h7pWXmXPxBQSGDUcLBrFjcexYFDsaxYpGsaMRzNY2hGUBTpvj4Ig9KPjVr/CVV9CTaNdOJln15OMYaWlkHnwwwrQIjtqb1vfeZMFvLmPo8y/1qHOdJEmStHVZlsWKFSvo378/EydO7Ho+EomwYsUKKisrSU9PJzc3lwULFnDAAQcAsGDBAiort0/9e0UIIX58t53fqlWrOPzww5k8eTJ9+/bdJq9hVc1FdLR2v1HVUtUU1qmwoBvrVFzQQDOcRU5d23VnMZu02ayOdlr+/hj+/Q7Gu+c+JL58GyE0lLxSyCpw3mucDmrzrx5D0QH7kFixkqL7H0H5iTPgZlsbrdO+pP7tt6ifOZPWxkYsy2LQcccxcPxduLKyN3qssG1aP/sfTR99QNuSpdSurqWjpRlam0lXBNlpATzFJdgd7djRKHYyiZ1MYlo2JhC1bMIJk5hpgW6guD2oaWloHi8YBmpnveZUVZDEnFmMfughCs676Cdd88YkW5pZ/czTCNtGDwYxsrLQM7Mwspybp6QUtZtUkS3V+NabhOZ8T+n1N3add8W4W4mGEgz44wM/q4olkiRtX9sjvtjcMVxXVUWmuW1SF9foOg9WVGzyOpubm5k2bRqHHnooHo+HqVOncuWVV/LAAw8wcuRIjjzySO69914OPfRQHnnkEb755puuagwPPPAAs2bN4rHHHqOpqYnzzz+fe++9d7ssUJPBbg/YaxoRsciGG4QAYYPpVFIQVrLrMZaJ6HqcdGa7LDO1PYmwt/6Ctd5AUVVwe1FcbnB5UAvL0PqUAc5X4x3vv0myZiVZ512GYriwOtqJL5pHfMFcrPY2hJnETsuiecq7BAtL8JSVkTvm2h17USl2PM6az/9H29SphFcsp25ZNc2Njdgd7ageD4rbC4bLmUXWdQxVxaNpBIIBgjk5uHKy0QNBVJcLxWVgxxPOQsh1zH3xBaL1tZz07fe4i3bMP85bW7R6GSsfe5TiK67GW1pKomY1NU88glrQl5LfXrmjhydJUi8lg921WlpauOqqq1iwYAG2bVNUVMS5557LGWecAcDUqVO56667qKmpYcSIEdx3331d51q3zq7H4+HSSy/dbnV2ZbDbA00PTyBRtXjDDVtS+ktVnHJlqup0XzNcKLqB4nKhuNypm/NY9XjA7UZ1e1BSN9XtQfF4nZvbDUrv6H2i5+aj+QPOh4JEDJGIQSKOOftztLIhaJVDu/ZNrFpB27//heLxoAbT8AwYjHvgbmjpGQA0PfEnohaItkasqmXkX30zvuEjd9SldUsIQXjO9zR//CFWONJVTk3RNLRgED0tDT0jE29JCZ6ycvR1kvs3peV//+XDk08kZ/AgRn/yWa/pHGfH4yz/0x8J7jGSnGOPo+HJPxNHx1tRSc5RR+/o4UmS1AvtTMHuDds42H3gR4LdXZXM2e2BnGvu2CrnEamOa5imMwOZTEA8jh0JI+Ix7FgM4lHsWAw7nnocj2O3tyES9dixOCTi2IkEJBNdjRd2dUII5xqTSecDhKZhlFbgGTYS715HYs2YDKqKVjEEcBaz5V5720bPl3XeGJqeepjGqmryjj+Z+gfvpfSJf6J6fdvrkn6UoigEhg0nMGzrdiXLOvhQ9rr8cj5//HFmXX0lezz5117xVb/qdlN+2x00vDGRupdeIPeci2h85jHavvoKX79++Pptn/wvSZIkadchg90eiP3vLeym+m63KS4XiseP4vWieP2oPj+q1w8en5Mrum7ntM4OaYYL1e3p1f2ofwo7GiE+ZybRWdNpe+UfBI48AZ89BxQFrXzwjx6v+gMEDhqNq3Igy555kj4nnkbthNsouufh7TD6Ha/i3j9QP+k1Zk+aRNYeu1N2+W939JC2mryTT6Vqwnhs08IoLKLwyBGs+MujVN59D3qg+8WHkiRJu7qdqRrDrkQGuz0g2tuxmxs3ttVpGpFqICFMJ1dXWJYzo6awNtVBwZm5XC/PUqDoupPOYBgoLsN53HlvGKiGk+aArq9dz97ZprgXzNqtR1HA5cFluHHvtycZJ51Cy/PP0jy9jox4DJeqopUO/NHT+Pban8gTD1Jx1dWsfOVVgj4PjY//CXe/ARvurOt4dxuBUdBnG1zQ9qcoCkOfeprEOefw6e//QPqI3cnc74AdPaytpu+YX7PqqScou/5Gah+4m4pbb2fp3eMZcP8fesUstiRJUnfkv249J4PdHvAcc8YGC4EAEMJp8xuPI5JOioFI3bp9nIw7C9TWO4WT2iCSSUQi4aQ3xE1ERxhhtoGZdBa6CdvZV0tVctB11ulJvIsToKgomo6amY3epy96ZiFaehpi2XdknXgcseYwTc88SnptLf6jT0Mr6d/9mWIRcHlQVJWs88fQ9ORDZA4owUwrwmxtJDn9qw2PSSRo+feLiHgUV2ERgQMPw7/fQeiZG6+ysLNLO/hwcvccjlhUxfsXXcRpUz7tNbVpXbl5GOnpRKuXEdj3QOKL5lF45lksGXsnfS+5FG9J6Y4eoiRJkrQTkMFuD7T97WGSy5duuOEHM6tKZ1mxzvQFPVVyTDdQOxeVdd4Mw9lPN5zZ287jUs+v+xhNRbGF03LYtlFsCyFslG2UrL4jCCuJHYsimhsxV1YTn/ElItSOHWoneMJJeIJB+jz4d+rHXkN00UKyr7oNLTMXc+USEt9/Q2zpUuIrV2B3dOA/8BAyx9yM6vMTOOQIkrWrqJn0Kt6zf423qGiD11Z1HV9ZGSIRJzzzG9onv8+aia+AbeMuryT9hFPwDhvZtYisO2ZzEy0vPUvo6y8JHngo2RdejmpsvRJcW6L0hluJ3Xg9pmLwzgnHMeioI8gcMhT/gIF4KysxNlEybWfX56JLqBr/O/pNuI/a+8dSeNNYXHm5NL3/PtEVK1DdbjIPOoiM/fZHNXpP8xVJkn6eZBrDlpHBbg/4RwzDKsjsZotYJ8DVUIRAmElIJp2ZWNsGy8a2LEgmEck1iHC9k+YgUpXLhABbOPdCrP+zLZw6vroOqoZQdSfg6kqLUOkdX2wIp+RWKjjUMjLxDBmJXlIOtkXbH2/DP/oojOinFDz4d1qfeZjaW690ghjdhZ6Xj6uknMwjTsYo7UftFWeTdtblaGnp+Pbcl6YnH6Li8l9T//Ykot50pw6yboDmcrpzWYJw9XKEEPj69iX3+JPxV1Rgh0N0zJhJ0/N/x2q6D6OgkODoYwkedBiK10d8yULW/OcFYgvmoRgu/Icdif/cy7GWzKP6gtPRgkFyx1yDb49RO+Rd9e9/COlDh6AuqSIwcARVs+cSmfwpdjyGgkB3u8noU8juV19L9qGH7ZBxs3ufAAAgAElEQVQxbinV5SJ9/wNZ899PyDz5DNZM+jdZv/glfS+5FAArFqP1i89Zdv992PEYJb+9crM6v0mSJEm9hwx2e8Ay0rA8G7Z4FUI4aQbhOCIRRthi3Y2p/FzhdLmydYRQQTXAcGZosS0UVUkFy04gq+iaE9CqqrMNUITpVHGwTCdV17ZRUrV8ewUhEPEORMJC+DIQbSaJWY3EZkzF6uggcNG1RF59hmR5Jd7Yy2Reeg1pp5yF6g+gBtI2OF36EUfTcPdNFP7xKQCyzruMpicfou8N41ESUae0WTIGidQtGYcDnaoIkdp6Gt9+neUrVwMCr08n45AjSDviZOIL59L2ziRaXn4OLAs1PQP/gaNJFFXSMX8B0aWryMwqpK01SrJ0N9wBP3V/fRTaWvDtsRe5Y65GS0vfbm+roijkn3UOsSefwh0Pkb//PoCCkZWFlp5OIhandua3vH3JJRx2+22UXHjxdhvb1pBz3PEsufUm+k24j7b338QKh7u6qmkeD9mHH0H24UdgRSIsvv1Wym+6RQa8kiTtkuTM7paRwW4PfD1uLB3Ll298h87cWUVdG6QqatfPKEr3C2fWK3Vso4jO5wQKIvWzkyuskAqasXvFXO56BBguA1+an/SCXLIGVxLoV46CSlLzEpn8DsaoQ7CXfE9oTQv+8DPoR1+AYridnOnGlYj6auzmWqzGOnwHHUbk229o//Bt0o46IZXOcCRNf/0Let6GeauKpuHZbQSeIcMIjDAIHPNLZ1hCEF21ijXvvkbDNRdhpxdCIAPXsP1QXS7Cq1cTW7aKguOPp+Tc87p+xwXHHgtAeNkyat98k1hdHR3zltB68Vm4PG78o/Yh+/wx6BndfVuwcVaog9jCuUS/n4UdiaB6vKn0GA+qz4fq9eHdbTh6Tl7XMYFDjiTw5utoReW4vTrYNlpmADKzob6BvNwsMs48gyn33MueS5ey29337DKLvBRFofCc86j71z/J/dUFNDz2J3Iv/g169vofTDWfj/733OcEvDff2mtylyVJkqRNk8FuDwzcuz/xUn+328Ta8ghrg9dU0Cqc8gvOf5TOZhI6aApohvNzKkUBTXc6h3m8KN6AU77MH3TKmnm84PWD29fVFrdXsW0i9fU0zZtH9ayZzPv3RxBqR0Gg6VA++mD6ulzYrgCGYhFe3oj7pYdQ3F7M5mZsWwF/Boo/DS1/EPE3XyTtoINpfX8ivpH7oOfk4ttzHzxDhnW70FAkE0S/n0Xz3x4Dy0TLyMI3aj9clQPxFRfjG3MNfS69EvH9p4iWOsyKvbFUN77i4k1elr+8nMqrrwYg1tBA7aRJhJYsoePbubROPReX24Vvj71IO/ZErDUtmE0NmI2NWGuaMVvXYLWuwQp1rP0AZLjQ8wrwDByCUdAHOxrBikURrS1OvnM8TsMTD1Hx0ttdAauiqmQffRwNn35O0ufHU1aOu7AYc+Uy9LZm0gqzUfqUs69lMv2112mvXsa+f39uq7b83ZYCQ4fRMOl18HjJOuMcWt+ZiNnchOr1EdjvILxDRziNOnw++k+4l0W330rFrbfjzs/f0UOXJEnabArbLmlx15je2DK9MGLadvwZGfitWPcbVTWVU6t2dUVD11NVE5wZXmdW1snhFakyZYppObOStoWwTbDiiFgHhC1nNte2nMAsFejQNevbO/kUlVyvF3V4JupBxzkfAoBwKMm3/5rEkv9OJbuyPwNG7Y476MUuHIyWkY3nkDJEuAOreiHJ6qXEFs91ShoX9MXbp5bmp/9M3i13oyjKJptKBA4aTeCg0QCYLU1EZ3xFx+R3EZaNZ8BgfPsfgjZiNCIRQ5/5IXoshN1eilIyGAKZJKoWE576KVZbK3pePmlHn9TV0Q3Ak5dH+WWXARCrr6fm9dcJL1tGx/cLaJ87B19xMXp2NnpmNp6hu6Pn5qPnF+DKL+x6LzZH7f1jaXnh72SfszYlIXDEcUS//ZrAUcdAMJO2r78iWr0KACPTizZjKn3OOJM9EwnmfjaVj447mtH/eR2jhzPPO0rfMb9m1ZNPUH7LbbjLKgCwwmHCX31B/WN/Assi58LL0TMyGXDPfTLglSRJ+pmQwW4PJObPQcQ2DHbXft0rUh+NUrNpnR/BFCX19Np6u07tXcVpG6woa1MeNC2Vs+s0ncDQnYYVbjeKx4Pi9aJ6PCheD3g8KEaq4kMv+UhmR0PYK5aTrGsiuXyl86Si4k4LcMBvz6F59kLmfzWLT59fTMXQAWTOmYsrLYiIx1EDQZRgFmphEXZuKercrxCff4R7yAispStpf+d10k/4xWaPRc/KIXjk8QSPPB4hBPFF82n7zwvY4RBaTi6BA0ej5uQQmfIO0VdfQsQiuAryCO59APqQEzGjJm2vv4QV6sC/30F4R+7jfBBK8eTnU/HrXwMQra2l/r33aF22DOrb0UMWmQWl+CoHYqSlIYQg1tBAeMkSwkuXEq6udjrNqd1nWeUccCBtzz1GxilnoAWcFsOKbpBz3Z3EZs8g9vlHuJJJfIMr8e59AMl4kpV/fhDx5quUXnMtClA1byFvHXE4x7z6b3wV/bbsF7oduXLzMDIyiCxZjK/SKUmn+f2kjT6KtNFHYbW3UffIH+hz8zg0v18GvJIk7XIUVelax7Mtzt1bKUL0jmnC7dG7OvH5K9BSs+GGzuoJonPB2doKDE5bYAsrmYB4AhGNIqJx7HgCEU8gEqZToUHYYDmNJjp/JUoq+6GrIYWqOAnknT8rnQF1L/sDVVWnDJvPg+pzoXg8mC1toLrR+w8h3pagffF8vvv0W4yCPuiBAKKlCREKpRb7qSgeL5nxdvL2H4a/dCCufoMJz5tP5nmXYxRuWHasp8zGekKffYLV3Ih35D54R4xE0Q3nd9daj1gxD7GmzklTKRlCdGUD0W+/RsvIInjUCRj5m14gZYZCrJk+nTXffIPZ0QGKgjs3F3+/fs6trGyTKQbf33QT2X2yUZMxCm4cu8nriHz9BYllizEGj6DmP68RzMui4Npbqbr/Hhpq6ln+zdccOnYsRb88a4vfr+3FTiRYfPMN5J54EpmHHb5B3nF8+TLWTHqV/KtuQlEUzFCIxbffSsmVV+Gv7L5msyRJP2/bI77Y3DHcUb2MrG1UbrRF15lQVr5Dr3NbkcFuD8QmPo1dv6qbLaIrGHVSFZzAVxHdNKBgbfxKZ7WF1L2TAqE5s72dqRCqkkqLUDaYEbZsGyWZRESj2+R6tzchwFzTRnLFKsSaNc4HBTrfKwUjNwOh6OgDhmN5sjEXfEtjcxxT0fGMGIkrIw2zeiHm8qWYHe0smb+cygwdX1E+KyMGJUMHYagu8m743XozrFv/OgSJ9nbc6emIZAJR/T2iZjEIgeXPJbxkJWZTA67SfgRGH90189qT81utLSRXr0TE487Mv8vl1GV2OaXbTNOi6vEnCLSspOD623H3H/Sj5237z7/An0bNG28RzM+iz+13s/yhPxGNRJn+0ksMPuwQRj78KJrXu6VvzXYhhGDNlMm0TPkEX/8B5J9+5npjDn31BfHqKrLPPBdwUh1qX32FWGrxaWC33cg65FBceXndnl+SpJ+XnSnYvXN59TYNdu8uLZPB7s5se/wxhidcjAi1bnS7UDVAW6cCg4ZI3Tt1clM1ZA0Xiu5yFqJ5/ShuD4rhBpcbxeVGaBoKipOiKyyn21oijkgkugLpDW69ggAzgRLrQLGTKD4/is+P7QsSeXsSSjyKXpQHlo3ebxiUDyPyziuoQafsmJqZg2u3PXGNPABhJWm653qmT5nBiH0HYOheVvpLSCyYS35hDmp693moakERar+BKP71A1Al9aFE1fVU4w/n3oxGaV20iLaqKux1/gHSvV6iTU0UHXww5ccfj6ppCGEjVi9GzP0CdeQRJEIJQlM+wA6H8e6xF7499sKORrBDHVgd7dihDudxextmc+PaMnY4NYiNohJUj6er455IJJzgOpkksWIZIU8WrF6Ox45SMP6Bzaqu0P7uRIRpUvveh/jzs+g79n4aXvs37d9/x/cffIyuweF//wf+gT8ePK/3mxWC+KqVxGu6+WZkExRFIbD7Hlu8UC68cAH1/34V1eul8FfndJUca3n9FYz8AoIHHLLBOENz57Lm0ykkGhrxVlRQdP4FW/TakiT1DjLY3fXJnN2eSERQ2EhgKUAxrc0/1zpxh1AUROeT6z5O7SRUY/2yZqRmflGcfN1eVJlB0XWUrDzUghLIzHXiu9YW3JXlJJevwlzdgFFeglk9FzWRJP2qcai+APaqpdh1y7E7moi/8Sx2exu+3Az2O/Mkpv37TQbtMYCyQhX95GMIFfZ3Klv8gLBtRO1KrJnTEJEQqBpaSQVqxQCE4cJKJBCWhW2a2KaJME00j4e8UaPof/rpaD8IyIQQrP7sM7647Tb8BQUMOf98PH0HIvr0x57xProQZF1wOaAQnTWd1tdeQA0EUQNBNH8QNZiGnl+IlpaOlpXTo9notjdepXDgbix45Fv0dIOOdyeSdvxpP3pc2nGnEvrvhxSMPpi6/37OyvG3UDz2foJ7jMQOhWhq7WDiySdx4A03UHrxpd0G0FYkQnTJYjq+m03j7Fk0L19Bc3U1ZjzhLNTsQdaNbVn4M9IZNeYyCn959ia713XHP3AQFXf8juSaFlY98TjpBxxA1iGHkXXamdQ/9iBGQR88/damLyiKQnDoUIJDhwJQ9fv7Sba0YGRl9eh1JUmStgVFVVBlzm6PyZndHog+fSeiqXbDDSL1X3ZnxQSRCljF2m2d1cgEYFlAKr9X2KmGE9bafX948q68h58DBcXrA92PSMadShWpdsxqejrJ5lZESwtGv35gJrCDBSiGga25wRMAtxddt1GaVpCsW4Wl+LGiMWbN+I7izDQyDjwKb8CF6g9s+NK28zpG5WBcQ3ZHMQxi874jOvMb7EgYLSsb/0GjcfUt7XbkdiJOdObXRGdNR5gmqtdL8MjjcZWU07FyJfP/+U+SkQjZu+1GsKSEgE/DW/s9xr7Ho2Rvfh5xoqOD1qVLaa+qIhEKdbtP7vDhMOVdjMOOY8XDD5A3sJzcK25EC27YfKM7ka8+J7ZwLo3fzMLtc1N6758QpsnyBx8gnjT55sUX6Tt4IN68fKJtbcTa24m1t5OMRBG2jS0EqsdLMD+fnMp+5A8bhq5r2B0dm32d4PzpNyxZyvyPPqJkYH92O/8Csk84aYtrAC+7dwL5p5+Br/8AZwb79+PJ++216BndB7OxVauon/gapVdevUWvJ0nSrm9nmtn93crlZG+jmd1mXeeu4lI5s/tzt2buEqyG+o1s7ays4MzACkVN3ZwZ2s6KYV33qdQDIWwUIZz8XiHoTMkFZW03YKWzyINw0hsQa+unKr0rDlbsJLreAQjMuEk8ZsKAvfFmZeBrmIfh92K6ckkuXYpeWoYWbUYxMlAbV3R9KFCz8rENH0oyhhH0gpbGqP1H8O2MRSirqhH5RaiR7mfhhW1jL3gD69mnnJbPmoaWnY+WlYPV0kJy4ssoQqD6/Pj22h+jqJjw1P+RXFWNYrjwjtyb7EuuAE132gx/9A5tk17BXTmIUddfj1BV2qqq6Fi5klWLVhJaVYc58QZQVNQ+lWsXG3Z+BlXWqducYgQCpFdUEMzJxlVagur3o7jc6+2z8JVXyHEpFComWlEp8UiMNS/8jZzLr92s34NvnwNRfT5y4jE6mtpZcPYv6P/M85TfejtN777DvokYNctXEY/GCRQVk79nAZ7MDFxeL6qqYoc6SNQ7/1tRVBW3x01g6DDcxcX05C9WJOKIf/2TrHN+RdU3M/lo/N0MeudtSk8/g8zRR/Q46C296RaW3Hoz5bfehpGVTf5VN1L38P0U3jyu21QJT9++JOrrsU0TtQel3yRJkrYFZWPNqbbSuXsr+a93D/jzM0naG1kM1lmJIVUPV3R2PbPF2ig3tdhM6aqkoKYaTKgomrq2VbCWSlHoinzXWZympY7RO8uUpRa19RKK14uSloYIrcFa045oqseOLSW5OMHKVg+uyArydytG7ZOLubwatawSPZBLsqYZu3G1U41hubOIUPUYzqKtzDxoaGPv0cP55oNvCR1Zhjdrw2oIqq5TuPfepJeVdT0n4jES82cTn/01SnYBwp9ObOE8Em3tRF98FiwTNTsPLZgO8SQdUz+nY+rnzrG2haLpeIeORM/MpOW5J0EIAgcdTuYR6wdqor4ae+7nEMhEHXIASiADIQRmUyPxJQuJr6jGbGxYW2+5pgot2obQNRLt7Yh4fL2qHBUlBaxujWA++Sj9f/8os375f1SUVxBbMAfPoKGb9bvwDBuJ4vUj3p9E4NwLmfeLE+j/+DPkHHc8geEjUP/2V1TDCRAVRWAoYPi86FlZ+PodjLuoaKv841l63Y1Eq5dhd3RQccD+fPv6RJZX/YEhn0ym//gJPao/rOo6FXf8jqoJ46m85360YBq5F4yh4S8PoPoDpB1x7HppDQC5J5xI49tvk3/KKT/5WiRJkqTtT6YxbGXCMiERg2QMElFIxhGJGCIeQXS0YYfbEaF27HAHdiyOiMedrlexGHY8hojHnfqpqZlep9NXZ1KEkpoaTk1iCpEKlhV6lAi5sxKAlYRkHM3QUXUVxVBQMnOgqRasJLEhh1P/3N9RVZvs3UoxoolUdQoVxedF8brQXBrCSmLVt6EXFyFUF/GQjTvTg+JPpzVupLrarc9KJKlfvJRQKIqeV0jefgdQMno0/lQN1uTSBUQ/+xC9fADeA490juloQ0vLQFFVQvX1LHn+OZqmfY6IxdD7FFN68mnkFeQS/W4mVkc72DZ2SxNWSxNqWjreUfviKipB9foQySSxOTNJzJ8JyRiKPxN9wHA8A4fgLilDz80HBezGGhILZpOsWoAQCkpWIUpmLqrLjWI4lRkSdbUkG+ppXObU4w0O2I3wF1PIHlCB4vGiZ+XgKqvAKOuHnpu/yaA0WbOK1pf+ju/YX7D46t9Qet0NZB57Yo9+tXYsRnzpIsz6btKANkVVCRxwiLOwE2j5dApN776DGcxgxquvstcxRzLgnvtRDaNHp41WL6Pmn/+g4s5xXddudbTTPvl94lVL0DKzSD/qBFxFfRFCsPi2Wxhw3+97NnZJknqFnSG+6BzDuNUryba2URqDpjOuqHiHx1HbgpzZ7YHE/yYiGjZSekzTUQyP0wiis5oCrP3GVgCGgWq4IacIrY/bqcBguMBwp6o0OD87z6cea7oTQCdjkIinAugYIhlDxCKIcAci3L6d3oFtS9gCu7kOs2YFVusazLiFHQohmpZhtbfj65uFZ95kyu+4g/jkSTTOnEciHEN1G6miFAqKniq4bdt4XRrZbgM9Pxe1ZDDJ5fNxmVHyDjkVfeAeqeoXXuc+FfCUAVZzI7Hvp9P01ZfMvnESMVvBVVoBuoHh9+NpmoXx9lukDRuB1m8wS1/8F7HVK/AEA1QceyzDn30eJZBG9LMPWf7eu8yIqxgZmfQ/9VRyhgxxKi1EIyTragl/NpnY9Gmo6Rm4Kwfh7jcQz6BhWKF27NVVWMtmE533JdGuvycFNZiOUVaJMXRvVAXs+uXYK79DmCZ20gTdTbx+De49DyDf66X5kw9pqMsmubiKsptuw11ajtXcRGJ5FeHPP8FsqEckE/j2PhD/Pgds8Hsx+vQl66IraPnbowx+7gWWXHclodmzKL7lzg1/h8kkZnMj8aolxBfNw46EMUMhYk3NmHHT6SrYgzQGOx7H+/479B1/P6rbQ9Yhh5FxwEHUPv8cI0YfyjcfTkZRbqH/Xfeiut0/er5O3rJyso84itVPP0Xfyy4HQAumkXnKGQCYzU20ffg2ZksL+b+9jsCw4XTMnk1wxIjNfg1JkiRp5yBndntg0VF7YrZsovTYOgvRuqw7Y5bqlKZ0dUxLpTV01dNVUDpXq6vrdFVb9zzrzcD1soRdQFE1khjYQkPTFYw0H66gB0+aQXzpSvx9slB1UI88C/vLj1DtiPNVO8JZ+Ke6EJoHFIXGL6ZhuAz8A/uh+tKJN7Ti719EPAxaRjaYSYTplO0CJ1/JyM/HKK5A6z8cJddZNGbVriQy+W2M0n6oe+xPpK6OcF0drTO+xlpdTcVZ5xDYfa9uqyXY0Qjhd17FQmF1GJrmzcObm0vlSSeRNWBA136JldXE5sxG9QecagwBpxqDGgii+vw9qkIgwm20P3UPxmGnE1+5HLO+lnAkxqrFy9Fmz6Dkwotw9ynCU1SEp6gvWkYmiqrS+vqLKC73RrvM2dEILU8+SPrp57Lqr08SmvkNrtwf1KJVVVR/AKFqaOmZKIYLV24ugWHDCew2FM3v3+zrAKdJxLIJ46GphoqHn0T1rT2++aMPWPnmm8z9chp7HXYwlXffh+rx9Oj8da+8hJ6eQc4xx3a7vfGZx8j8v7NQvX6W3jOB/nfd3aPzS5K069uZZnbH16zapjO7Y/v07ZUzuzLY7YGmG89BtLdsZKvi1NntrKurObm5QmFtIwhI5dgqKGoqjZfU8rV1KzGsXcXmdGETa7uq/XCxEkKkFsH1DkLREIYbkUhgRuMkoibJcIxkXQ2KruNy6wRLcnD53ehHn4GYN8upVRzMRCvpj95vN9TCEpKmSWLcecQbWrEVgeXyoJXvjta4guB+e4Lbt/ZFFQV0F0I1SLbHSNQ3INY0oXldGH2LMcr6o484kGTVQqJffIxv9AkY5QM2fhHdMGtWEPlgIq7d98EurmTpW2+xZvFiXIEAFccfT+7w4ZuV3yqEIFxfT+vSpbRVVRFtbsaMRjFjsfWOz8n2kdG4irzxf6bllX8SmzEN/YyL+P7eCRiJOEoi4eT5xuMotgW2IG3wYCouvZhk1SKyzr+82+BdmElann4E/6FHoeYVYsfi3Y7TyM7Gql1FfPF8kiurnb9jAMPoUR6vHY+hBdMIhRK0v/8WA/7xEnpaetf2xrffYuV777Jw5ixGHrgvlePvQfP5NnHGDVX/6Y94y52ZXj1t/WoVidUr6fjfZLLPuoBlD/yRovMvwJWb26PzS5K0a5PB7q5PpjH0gOo1EOHu3rJUIq1tgW2CBSQ6n0/5wf+/dwao9nplxZTU7LBYpx1wVymGtQvWuhatdZbe7R3BrgBU1Ua1Q4CNbgi8HgM114VdlkFkTYhIdS3RljCKUIj/5zna1VxEYT/UGZ9jRN9E15z3XNVUzKSNLzOAC1Cy06j5+ms8wsRzzOl4Ru4HHi+K2wMoTppINIS+ehGebC9ClGBbEG8KEZ82Dd6bSPrV40m74Gqik98iNu1T/CeeiRpIQwhBrGop7ZPfJTbnW0gkwLawDQ/KgN3JPPhg0oaPIHjBVcRnTMV871Uq8nPxnHYjpqKx7L33WPDyy6iG0X2A+YMPOP78fNKKi0nPzSW3bxGa243mcq0XRM6ZOImgYRP+4r9k//J8aldUo3z5CUdMfAPbsjAjEZKRCGY4TDISIdHeTtWfH+SrCy+m3623Yj9yP9m/vn6D1ABFN8i6/Dpa//U0en4hqj+A1dSA2dzU1fEOAWgqRnEZ7gFDCIw+tkeLyH4oNu87zE/ex/fra5hz2gkM/seLuPsWA87iMZFMIIRg1lczUMbeTr+xd6MFuikttxGl191Ax7czWf23v2KFQugZGWQdOhr/0GG4iopJ1KwGoM8551LzwvOUXXPdFl+LJEnST6GooIhtVWd3m5x2pyCD3R5IP+0c7NaGbrcppGZgbQssE8w4mCYikUQk45B0OlzZiVSnq9QNy0LYqYVotnCOt0XqXHTN5ApSaQ2qujYFQtOclsO9pf6YcBb42dE4tpVAUW0n/NcMFDT8mT5c2YPp+H4Jra1hMisLKEiPQXwuZCvYyXSseBIzlsBOmviCHlbPrCKjMIs0t0bZ4XvQuLCJ1Q/cg63oTvm3ztrIqU8OllDA5UEJpGH4Xfh8Tlq1qXhoOfcUKCzD7lMBlonrf79xAjxFQcvIwrfX/mSNewC7oYbY7G9wDxpO5LMPiUz9lIY330RYFnpaGt7iEpS2OGv+n73zDpOrrNv/5zlt+u5sL9lNNsmmd0JLCEF6B1FEKYqKiEpRQHwBFVAQRPzZO0VEpYgoRYooQUJJIAmk92STzfY+fc6c8vz+OLObhGyA+EoMeee+rrnO7pxnzpw55+zO/dzn+73vn/8AYZuUj5vMyM9fihoI4qRSOOk0Tjq/TKWw4wkyO5pxs1nvOHV3QTyGMqoBiiLeuLcdylLbYtni1RxmSUJzj6H6upvY/plzSa1YRmjGbIxIBCOye0pc7dy5JNeuYcm55+A2jCH46uvM/NHPCIyo322cEIKST36ezMo3EbqOb/xk1JKy/xWhfSf4J09HLS1n4I/30Pj/fsy6T36cMXf9kKJDjwCg8pyP4pomruOw4s2VqHfdger3Y1RVE546neDkyWjvEMkshKDokNkUHTIbAKu/j74XF9D1+F/w1dUTHNNIdvNG/I3jsfr6cC1rnxviCiiggAIK+O+hUMawD7AWPILsad1zhfRIqtj5q6fQDaabKfmlNvi7uosKJ0DTvQY3RfGS0YbW5QMq7Bwym0VmkmB6kbCu5T2HmUXmhr+V/IGDlEgz4zXiKR6RdxUVrBzEOnEzDkpJMQPbu/CHDRKbWglWRBCKgpU2sTIWQtXRy0pQ/Bp+YZLqSXkpXGURTNOk+PwrEKqGEi3b871zJnZnG253O25/L2oohFZZjTZqLLJ5DarIkUs45LIS38wjCM4/EaEoSCmxtmwg8/pCZDaDNnYilFfjrF6GdF2Munrspo0ETz0X6QtitrXhpJLYqRR2Iklu83rM9atwbQfV70fx+9CKo2ilZWil5WjlZfiKowjXQiYTuMm4d5zy5S4iFEItKUcprUAtrfDS1nx+1nz3Droff4hZN3+b4lM/jNncRNcPb0cfM2GXmwn5lD7XwaitI3rWuQCsuvhCLFfS39tFYIb8YtQAACAASURBVPIMxn/6M1TMmvVf9WF0U0n67v4xweNPY8v1X6X2qmspO/m0ofVtD9xPy8KX2bx+A6PPOIOamTMJujbmls04qSQyl6Pu8i+jl5W9w7vsjqbbb6PuC1+k75EHqPri1cSWLiWzrYnqcz/2fnzEAgoo4ADEgVTGcGtnK2XOPqS17gN6VZVvVo0olDH8X4e5eQ1Wy/a9rJUI8o1mmoFiqAjd83lF03Z66CqeP+5O1wXD89uFvKrrgusgHcfzjJWe6ijJB0qoGsLQUIIBKC3zyLNQDxplF1X1nCl8Ac9+baATt7cTq8lB8cWxevopHVNDrK0ff205+PxewENxEa7qI9fThZtMYaezOJqDP2KQjWfI9sQxqotp/vmPCMyci6Lt2fAlHRs3HgfpohaVoGkqYv0m5JKluJkMSnERkahE6kWktzURf/lG0H1kWluxLIlSWYPQdJSuZRjV1WR37MBNxhEvvEh07hzEK/9AKCrBkz68W6MVnLHnfiQTuIkYMj6Am0oiAgGUcKVXNuELYHZ1kd22DTuVgpyJu7kZN7nae00qgb+6kinX38ArzVvY9t1bmTDjcPwjR1Ny9sfIrl8z9F7CMDDqGzAaxuJKSedPvodvdCPTH3iQll/+FPuJJ6mpKKLjuWfY8MgjhEeMYML55xPch7pVx7Lo37CB7uXLSbYM52byDhCC0aedRvm0aSihMGVXXk//A79m9PU3sOXb36bk+JOGwh5qP/VpZC5H5bRpJBIJNt93H7HublzHQS8uJjxqFNb3bmfcd7//nkl76QknElu82JtsOg7Fhx5K51/+XCC7BRRQwH8FQhHvYxnDwUAkhkdB2d0HOK8+hOzdy5f1YEMZeAlqEqQUSNdF2q63zDle+YK1s4xBWhbScjyiuwdcBlmsGKwLBhQGa3rFLiEUBwcUPU/kFRC2VyIgDR8Ei3DbW7H7e3H6UiihINRW07doJWowiBoKEqivJjjzULSG8cj+Tsy/P4zVnaB/cxehymLS3QNEx1ehX/7DYdOyBiElON0dmOtX4vZ2e2UKpeXYm9bgZNL41DQiECBTPgWloobI1OkEx49H3YsTQGZbE12PPEj8lZcwKisJFnspY8qI0SgNE9CKIqjhMELTcdIp3HQ6X8rglTE4yQR2MjW0PaFpBOrrCYwejbaX+N/OX/wAquqpu+IqXvvkOdSUVVN9+dWUHDVvt3GuaWLt2Ia5aT3m5vWUf+Fqsls3M/DkY/jGjkerrWPt/3yN8iOPoPJDxyCmzWbjww+T7u6mYuZMfEV7vr90XWJNTWS6u71zqmmUTppE+YwZFI0cuU+e0K5lsfkvf6Fn1Srqjz+ehpNPRigK8acfY+Dll7D1EGNuvWO31/S9+ALxZcuGyj7UcBi9spLutevZuPAl5n7yQkZc+oX39P5SSrZ++2aqTjoR6bpEjjqGjkf/RGDMWIpnz37Pn6OAAgr44OJAUnZv6257X5Xdb1TUHpTKboHs7gN6bvg0VnvH8CsFO50Y8iEHKINJaPkxu0bACt5WrpBfMvhadWf8sKLs3PZgA9OuCvD7dOHvf0gUIZBmFpnv8lf9Ono0jKFbaI3TkIkenNZW7GQOXJfg3CNR6hqxN7yF3dqGE0sgXRcciRpQwXaI7+gl258gOqYa07Rw0lkIRPKV/ipSqF5piW6glJShFJfsZvUlXRerrR0hwDeizoshzvbiCwvUyXNAKLi2hZtOY7a2Ym5twk4kCB9+JJELvoBa7qW1SSnJvPEy6SWLENFS1KJi7M3rvAa0+nEQKkYNhVCCQdRgCDUURA0E0SKRfbbsslq2kV7yGj2rN5NND2CteYvS088j19WNFo3iJJO4uZw3WAhwHIxoEb7sABWXX4daVExm3WoG/vZX9LqRtD72GNIfpO64+ZR95osA9K5Zg50ZPlGwqKFhn9Tfd4OUkh0LFrDt2WcpmTCBSRddROLxh2m++zdM+tOTGOV7fy87Hie5dg0DC1+iadEisqbJvNtuI3LIoe/pvZvuvIP6y6+k5zc/pfqaG3Atiy23fotx377tP/XxCiiggAMYBxbZbad8WHHsf48eReUbFTUFsnsgY39cjK1fOg+zZZiaXfJdjHmCKhQlz293qrFeEhpDdZZ7GvLC7hHBe64TIu/lu6vn7sHSnAYgwXEFomwESlk1SjCEUVWF4tpYbzyPmokTPmouakjFXr8ex3SRpgWqguLzo5RVIEJhZKwfN9aPm0yghnTcnEP/mhYClcVgaASqSzyCuesDiXRdnJyN60jQFU8113WEpqKG/ChT5pB+cwO25XpBDKk4ip1CSgXXdhCKih4txjdmNGokROr118kNxNHKyogcfxpF534GNa/EWi3bSf/rOaRtEzjsKJzOVpzONi8OeshmDu/9g2EvQMQ0vX1MxHBiMZz4ANLaaUEjNA18QYQ/SPDo43A3riTyqctJrl7N65/5OHVHzGHMrXcBoIXDe6jbybVrab3/PtTWzYz45u0EJk4GILV8KQNP/ZXU9u30t3RSe8QsRtx463sOcXCzGcwtG0m9uZTUls37dEkIRVBx3oUEJk4Zeq5v3TrW//GPhGprKW1aTWJHB5Mf/st72t6273+P5Y8/QVl1JXPuu383G7O9IbH8LTLbmqCjmbKLLkENR2h/5GHsWIy6z106rINGAQUUcPCgQHY/+CiQ3X1A/70/Jte0Zc8VUiJd22uksrJg257aKt183W0++nc3L91dbckGSxLyCWCagtDyXr2amrcXGxq8k6C5uywPEkjbq1XGcXFdQS4HpulgDqSJjIqiJmKIohIicw/DXbccJ2V5DX6ujRACxW+gFJcgVQV72zak66KoCpRU071gESWT6lGOPg4lWOQdVCPgPbSgp+72tUHbZmSiH2k72DlwXBXXTOL0tOGUjcJNp7DaOjDqRqEFAkgJdk8XbjqDq/u8umpVJTi6gciRc7Bfe47U5q3YZg4lWoI6bhaKP+idc13Had+BE+tDDUVA10FooAikUBCGAUjImrjppFc7rKteA1s06lmnifw14ToIXUOoGplliwnOO57AtFkYE6fjtDfx+jlnUjL3eIzS8j2Pu+sicybhqdMIT5lC++3fRKkbQ/11N+CrrERKycCzT9D78B/o6+gjXFPJhB//AjVairRyHvmO9ePEBrAH+ki8uYzEps2k2jtxMmlk1ovN1ox989mVjoPlSPTSEsbeeBMl8+YPretYsoR1v72PomWvMO4nv6HoiDnvaXsbrruWZf/4J1Pnz2P6z375rvsjpWTrt26i7uKLSb25hNKPng9A/K23aPvDA4z6ytUE6ke+589UQAEFfLBwIJHd23s73leye2NZ9UFJdgsNavuAkku+/B/fpnQcyGWRZgY3m4FUAjfeh5uIQTKOm0ogU0lkOonMmUgrt1MlPgghpIWimODaSMfGsF1CtsRJBRjozeGmXMoqJImXXkUpryRYYyAiUexYglxvHDeeRWRiYBi4vXG0YAAlpCD7OigaN4LEtk6ixW+hjpsGgSAKAlSBIrMIFdRxoxAzPRVRZtO4LVtxtm1CxlycGSdg9GxEzDwb5cgPk3zsHsyN61BHjMc47cMYI8fiNq3B7etGlNeQ7Oqn88+PoUiH0jMvQO7YjB3vQ+tdR/CTX0GbeChuJoObSeOmU7jJuGdXZ5rIXAaZSePGY8hMCrW8Eq26Dq2mDqWsAqe3h2zzdtz0zlpe6brIdAo3FUfRFFLrN+B0tVM6cTpqzWgmXHoBW596idEX3U7ZrEOGJXmJ1avpfupJGDsFLRtnx7duxC2tpuL004meejbFx59C4I6b6XzxX7xx9lnooYBXNiIUpGXh5nLInImmgD9aRFG0CGPUWIKzj6DoQydgjBq9b6ESuRzt372FTEszm6+/llwqTdUnLmL0l6+m+rDDKJ00ideuuoIVF1/A0eub3v36UlXG3nQLdirFsn8uoOTe3zDyc5e982uEQA1HUMsqMbdtHXq+aNYsQpMmsf2HP8A/ciQ1F1z4X3WrKKCAAgooYHgUlN19QPpPv8Lt3DH8SlVD6LrnrmAYCE1H6IbnLLAvqVGahlD1nbZlg78P2papnnI3aFeGpiEUbZ+afg5kiGg5OA52axPOppXI5tWITB92Ty9W3MIqLaHvldWUTahCr6zAdjSEz4cxbiL+KbNxhB97/SqspvUYqa2Ybf1Iy0Uv8qNU15NcvprcQBKpGTs9dvPhHYqqoIf96KVRjMkzMI46BX3cNEQgjL1yIfaCR1GmzYL1b+BKDf2Tt6J0r4KRhyKMINK2cLo7cLracXZs9byXzSyW6dC/+HVyXZ0UTZuKETaQzesQJdWELrgCY8zuaWyuaWL1dGN1dXqPzg6snu7dxujlFfhGjkLZi39s/z0/JjhhPMlVq6n91vcw6kcje9voefR+ejbsIJMPSjAiYYonTaHkyKMIHXGU5x5Cvsns9cV03/8bZCaFNv1wcj09+KqqqPn4J3A62+j4/m0Iw0DxB1CCQbSqaowRI/GNasA/YzZOPEFi5QoSq1bhpNP5E7y3Mp29QHoe1LmWZkQ2TdUFF9H1+3vpW7uB8lPPYMKd3wdg0YnzyWRyzP/Hi2iBwLtuNrVhPRu+ewfrFr/O2Y8+SnjqtHccn1y7huSqlejSInLUMRj1o3Y/3q+8TNdTTzL62uswKiv3spUCCijgg4gDSdm9o6/zfVV2byitOiiV3QLZ3Qckf/cdnPa9kN38l/JgQIR0XaTjgu14ylfeN1Zo2pAVGYOkVffIseLzowQCCH8A4fcjDB+K34/weT8LzfCa1px8GEL+fQ4epVci+3uRVg4RCKE2TkepHeP56D75M7KLXsTK6cjqCvpfWoYibIqnTUQtG4Eb6ybX24PZ1Ys5kARHIm2LyukjkZYEXDAMFKl6KXdil9pYgdcjKASu7ZCLJcnFkiiaiq84iBYJ4Z93Ar5jzsR68m6UcRNR+ppw2tuxisdDLg0l9aCqSEXHSaexB/rRhE3g8Ploo8djr3wdu7uT+NI3yHT1IVMJAqV+dF1gukEIR6FiBKgaimGgV1R6j8oqjMoqtIpKL37asXG6O7E7WrA7WhGKhlpWMfQQ4SKEEDipJO2XfYzQp64i9tC9jPz1gyiGgbvmFWRvW/5zC1K2RuuqLQysWkPUzTDuez9DK9u9zCG1dDE9v78XM1SGGo4gkTjxBKEJE5COQ663d2fgRb6kQrqu11jnM7BTaS9Ahd2js9/bJeHVqEskZksL8UWvIMLFCE0jvnoV1R87jwm3fgc9WsySw6YTmzSDGV+7nspZs9510z3PPsOae+6mb/s2zvjnAvRoyTuO33LLNxn1lWvoe/SPVF56xR7r7USCpru+R8nRR1N+4knv/TMWUEABBzQKZPeDj/1axjAwMMDXv/51Xn31VUpKSrjmmms488wzhx27Zs0abr/9dtauXUsgEOCyyy7j4osv3p+7uweMIh84wylp0ruNi+ecIFEAFSkEUmjeOtdTy4TjIG2JdGyk5eA6NqTSSMfGdmxwXKRte+UN+eWuP+9sSlPeFh98kEBR0Uqi6HU1qM1rEZaJ4jNQx4zHN34scs1mpJkmMncesmUjXS8tQQ2sAqFgRCOERtVRfmQJQjpkNm+ne+U2VL+P4royhJvFzAl89Q345p4AwTDCH0Dx+ZGaBukE9rrlaM2bCSbiuI6Fazlke/sxn3gM7ZWXEBMOx/nzE1BcjqqZKM2v4URHINwiUBT0mjr8s6ahVdaQ+Mff6H/8z+iRIMWf/CK+Y+sJnHYe2acfQqlvJPvmYtym1ZSNKkeZ+xFyzTtwB90N7CS0J7Hbt2LvMh8ViopaXoVWMwL/7LkgJU5vF1bLdrIrluAmEwBoVTUEDp1D6l/PYdTU0Pmj71J93TdRpuy0HpOuS6S/gwk1NchptWzb2M7qL36acV//FqEZO221QoceiV5dw8BDvyVyznl0PfsMbiqN1deHUL1mTGUX9wqkRAJ6JEJkxgzCU6aiRfaeYLYvkI5Dz/2/QauoQFTU8sbpp5JYs5ayOXPwjZ9MdOMa2p55mk1//jOqYVD3oQ9RM3cu6jCJZ+WnnsbozZuIP/In/nrCCVQ2jqGiYRShaNRrOpOS4PiJVJzzUe+YRqNIwInHvL/ltzWmaZEI4759K60P/I6We++h7pLP/Uc+cwEFFFDAIIQiPM/992nbByv2q7J7zTXX4Lou3/nOd1i3bh2XXXYZDz/8MOPGjdttXF9fH6effjo33HADp5xyCrlcjs7OTsaOHbvXbe+PmVfmDz/A7RpO2fVuh+/0wvUaxwYjhHe6L8ih8WJXVTFPlr044LxvrqrmyxZUULyGJ69LLe+9q4h8aUT+IQ4KgR4JWKZKbkc7TsbEVQxEIIyW68U3/2TUDf8is6ULfeoU0lYpgeQmhFBwpYKjF+EESjHGTcfXOAHr7uvIpbIkN7eTTWRQNI1wfRVOxvYs3KTc6cqQj2hGStAMlPIKlFAEt68bmYyj6gKsHMLQYcpRGDKOUlqJbrVBxWioGoujRHF6u3F6u5GWhd4wBv+sw8gse4PkM49hVFVRctXXUUIRcgufReay6EceT+KnN6Mk2lEbJsCYQ1GKS1CKoqhFUZSiYkQgiNPbjd3egt3egtPb7e0r7DbREbqBWlqOWlZJ5vWFhM65iPhPbyVrRFEU0GvqqLjkS3s99s7LjzLgr2XTnXcw5pyPUnnhZ3ZfHxug59c/ovSiz6HVjCC9ZTOq349eWoYaDL77uXUcnPjAPl0PIh/F/HYkF71MavlSQkcezbKPf4yK085CLSoi/txTBEbUIIWKPrIBObqR/p5epONQOnkyEy+4YDeSKqVk0w1fw9c4no7Vq+nZspX0wACKz0dgxAhKFTji7nsRmkZ68yZii14jVF+LGi4ilI8rHg69Ly5g4JVXGH3DjUOhFwUUUMAHEweSsvvdga73Vdm9PlpZUHb/N0in0zz//PM89dRThEIhDj30UI477jieeOIJvvrVr+429v7772fevHmcddZZABiGQTgc3l+7uleopQEUOfyXulDy5FTXwfB76Wiq5nXWD3q5Diq9KICCK11wBdK2wLIgl4/KtbyHsE2kZSNcG9x8Y5ocJGX58oXB5cGg7kqJdFyEKvBHwyizZ6LoGk5HC6m1beQWPg1jDsFXb5JdvorwWeehjDsHJRJBe5vDgMxmcOeejrLgcYon16Bt7cXJWiSbOxB1Y9BqG3YdjZQS4bpeelmsD3dHGzKT8ti3riNUBVU6KIaNvnwhqRHjERsWIXIphFjjTUb8YW+8IsB1UOMtJNt24Jom/hmep2vHtZeilpRRfP5n0aNRzKf+SNGVt+BsXU/m8fvQ40/DxHk4YjR2+w7c+AAynUItq0StrsN/yBzUsgpQFNyeTlAUlJJyL7bYzOL09+D0dOP29yK7O0AoVF1yGa3fugERLaPn9/fgHzs+H16Rf6TSuJk0gSlTKe5ey6xf/5alX72G9Ia1jPzGbSiGZzGmFkepvPpGen71Q8LHnUJoyoy9nko3lcTcvB5z43rsnq4h72k1WrJPqoS0LayONopOPRv/pJ11teE5RyMMH+k332Di165j0y9/xcz7fofi2nQ8/BBFR8xB8/twVy8j0tWDUhzFEZIVv/gFM6/YWYIghGDsN2+h+8nHqaysoKK8DKEoGLW1uELhxdtuY8KrL1NyzLEEG8fR/ocHqP7EBXT+4gfvSHbLjj0Of20tG//nOhpv/hbaMOEbBRRQQAH7CiHE+9YIezA32O43srtt2zYURWH06NFDz02cOJElS5bsMXb58uWMHz+eT3ziE2zfvp0ZM2Zw0003UVtbu792d3g0r0ek30GZynvoSuROQVeAl3+meHxUqMj8c0LgkZV8AIVQlCGfXhQFNAV8Sp4cvO027GApw2DgxEEC6TqQSeKkMjgrX8N2XKQeJNjYQK4vAS1rSed0fNURzKceJXDZBJTSUnKrl2E1bcDtakF2tyJjvahTDseIhrEG4oTry8j2JkGAr0hFkf35xrRdFHlFevOQSh+ybiJS83t10fE+6OnFMQzv9n0qjb18CYybgUsQ0d+MCBWjBCsQoTJkzsRJJuh/4TXCIzZQevIp6LPmYyXSCFXD7u2m5/99GyceQwmF0V/6F/rYiejTj8VRXMS61xAbFyNGzcI/ax7amAnIVAJ763pyi1+AXA4pXaRmIE0Tp7/XmzA5DtK2wRfA1Q0yS14mdO7FJB78NVUXXUz/G0uxVA11YACtpASjfhRqSSlauAjh85F+awk9S9qQL3yVmdf9D+se+zPZL32Gsbf/AL2yGvDU4/IrvsbAg/dhrl+DEghgd3d5TiJDJ1GiBIP4GicSPvZktHcIfHhP14TjEH/mryT+8TTFHzkfo85rDgvNPhxpZsls3kjd8R9i9RVfYtYDf6DmzDPofvRhYsvfwkykEIYPOZCAdevISEH79GnUzD9maPtqKET1+Rfucg26ZLZuIbH8LSpKS1j1058w/5hjAdDLyrEG+lGLikkufoXwkbsn0u2K0ISJjLnh62y++ZuMuubagj1ZAQUUUMB/CftV2Y28rW4vEomQSqX2GNvZ2cnatWu57777mDBhAnfddRfXXHMNDz/88P7a3WGhVFdB/7vMfIbKE5Sdv+ab15BOnhC73jKfgoZ08g1VMt/cs/P2uhz05xUeYfbqgjVPKVZ1pOopxweHsgsiGESL1KCVa5Ds88imomBv245eWY+bVSEryfXHUXwKmXu/iwgEPW9iXQUUnKyFm7NxX1+APv9YlI1LkfEYWsCHoqrkYv2ocjCJTu6cMAgV/EFEKOqVK2RTgALl9RCtxF6+DDdnoQpQi0LQth4RjoBPR2gCOjYhwt2eW4bfjzNtKmZ7B9t+/Et8tY9TMm8u4XnHos04yrsTAGRXLmXgofvIrlqGuX4Vav0YpKMj473w1kOI555AqajDTaWwsyZuNovMpMHMeGRd1UAzwPB56rKqIqQk19xE0YeOo+jjnyXxh19izJ5LeEcTjJqM2dZCrrvbszzLZr1jbFvoldVUXXcTzpYVxJ/7C3Uhg/ZQlFWXXcy4G24mcvhcwJv9l1x4CebmDQifD628EiXw7mUM/y6EqlJ85rm42Syxvz5EPB4jeu5FaGXlhOfOxzWz4NiUOy6rr/giU3/6c0Z//6dI1yW7dhWpRS9h9fWT2NGK9cICVl91Jf5f/ZqSI4f35RWKQrBxHMHGcUzu7GTh976HtG2EplFx1ofpfvIJaj97GbFnn6Tjx3dS8bnLUUPD33kyyssZ/93vseXWb1F67HFE5x6F+h7cIgoooIAChoOnc71fyu77stkDAvuN7AaDQZLJ5G7PJZNJQsPEoPp8Pk488USmT58OwOWXX86RRx5JIpHYgzDvT7glY4HhYlu9jvGd6b+O1/HvOgjX8gita7+t/MDJuyjIIYIs3rbJnTHC7JKq5e4kwDIH5HYPqPigI9MPCbBzLnbOAc2PqruoER2rdRvq4Scjmlcj3UoUZ4BcWz+oPkTOQQgTraSI4OgalGiE+N8Xkm3txqcIHENHMSRCQMAPipbdXUVXBEJ1wUihaC6urSJ1A4JFqCVlCJ9O8UmnkHnuMezufjDTSLwAEFw/QjrIYAShSNBVZC6J4aQIjK0mevTRpFa+xcDChXQ9/y+EbaJW1WFU16DXjsB3+DEUfaQea9Nasq+9gJuII4rLEY2zcNqasDesRAn4McJBKA0ilbB3fOJJbx6keoRQ+AKeu4emI1Ipkotfw/jbowSOOp7U4w+i+AMEZ8ykaJdghl2RXP4mO269iRFXf43iuXMQFfWU+UtoufdXrLj2SmrOOJux/3PT0Hhf44T9dFF4UPx+Ss7/DE48xsCjv0erqKb4rHMpOvYkBp59klBPN6KohA233EzlrGloAT+Kz49/whSKx0+iuKMNq7cPVq9mxc03M/5Tn6L2wove8T1rL7gQceeddD73DNVnnEVg1CiyO5oRQhA97Wys7jl0/uz7FM0/nvCco4ffb5+Pxlu/Q8+zz9D885/tbEIEfHV1hCdOJDx12n+sia+AAgoooIDdsd/IbkNDA47jsG3bNhoaGgBYv349jY2Ne4ydMGH3L9HBOpL/tktaduMWnNbte1mbr1nI21jt9LPyOtNFfukRK9XzzlXyauLgUuQb0wbLEwb9c6ULjg2O5ZFkAUI6Qw1wQtp72acPHlQlgxENogWL0KVESgGqD7d9C1qxhrX47xhzjkNpbyG7PU74iEmoVSOx0xmsti6c/hiZli5kUyv+qhCZDSvRJk9C07txM924Fij+ABh+LwTBccG08yp63tKNAW8CoaqgqTjNOk46iz9j4ptzIsqqxdgd3ch0HGEEAAnpOMIf8BoFw1FEcRmKdLE2rUEFQmVB/NFx+MY24p8yA3PjGuTIqZg9/WS3byW26FWkZaEUj4DyBpztm5HblyACIbSp83CyaaxYL5qtokdChCqLUSsqEa6DTCUgl/XUXlyUcJj+RB/Zrg4Sz/yVym//mIGffJuyG+4k89KzhM74+LDHPjzzEHwjR9Hy/dsp/8RF+DctRTv8dEZffzMjPnsZKy/7NK8fP4cZDz2Ov7LqPZ9TN2dibtlEdv0arM72fbwiBIFpMwkdNmco3lgtKqbskitIvryAvj/cQ8mFlxA99SykmcVa+CI1F36SvrdWYPf0oIVClBT1YvcuxNy0nsoPzcNOpxlo3kHv1ibSN9/EmBu/vtfoY72klIZZs1h6552ccYbXQ+CrrsFsa8NXW4teUUnN124m9lxe5b3kS6jDeB8LIag47XQqTjt96DnpumRbW0itW0fL3b/GTnhigFFeRmTWIRTNmIk6jBhQQAEF/N+FEO+jG8NBLO3uVzeGq6++GiEEt912G+vWrePzn//8sG4MixYt4qqrruKBBx6gsbGRu+66i9WrV/Pggw/uddv7o1ty4L4flziSHwAAIABJREFUkWveuueKvAorXTfvgbt7A9mwXriDEcGDneFikCjL3ZvP5NteN+gegNj5Gm8D/8mP+t+BlDiWg5NzkMkeFFWglxbjryoiUBpCsXM4roPZkcF35OHI3gTZHdtA1dEqKtBKShFGEFyJTAxgbV6Oa0IupxGqUBCuQ647geL3IUqjefcLBal43scoKkouB2YWaefysc+e+i5VDTueQ6kaiVpdjVBc7OZmZG8XFJchZAbFdXAtL+pY6gGkaUHNSERvJ04ygTr9MNyNq3AUHd+kqWilZRjT56COnrTXQ2JtWUf22T8hIlHUkWNRK0cgwhGUUAQRinin3TSRZgZpmrixPuwdTbivPErLig58wiFwyOFoukLopLORsT6c7g6U4hJ8M49ArR25xz846Th03P0L9PJySiIZlBM+5ZVLAJ1PPEbTHbdQdNIZTPrmtxG6jnRdnHgMZ6AfJ9aPPdCP2bQFe6Afq7eHZPN2cskEjitRBuON3+sl4bq4yTiZjIkdLqZo8lRK5x9HpKGB6Nix5NauJL3kNco+dyVCUej5w30kF7+Cf+JkgtNmoo8ZT+/i14mtWIF0XUpDAkXVaH3un/TFksz69d10P/gHKk49DSUYRPH5UAyft/T70EvLiC16lac/+1k+uX0Hiq5jtrXR/eTj1H1hd2cLq6ebnnt/QdEJpxKaffh7/oxvR66nh/hbb5JYscJTgffRl1jmG1YDDQ0UzZhJaNKkoYlCAQUUsO84kNwY7kr0UP52XvAfQo9QuC5SflC6MexXsjswMMCNN97Ia6+9RjQa5dprr+XMM89k6dKlXHrppbz11ltDYx988EF++ctfks1mmT17NjfffDM1NTV73fb+uBhji1/B6u4afqWiDRGCYSGlp8zaOc/Cys7/nH8IOwe2tSe5HW5TiubV6yq7qMAHA9kFhJlA7WtBBCNgGNgtTVimjdnaStGEOsK1ERxhYG7tQpt1CErGRBh+wEVEigGBk3OxE2nUrg04Az24pSOxdrQSrg9j9yexEzkv5EN45QuDKrzARdqO58zg19GqKlCli5tJQzYL4RBOxkVUNiATcdSKCjAToPkRxRXIpjdh1ExkTzN0tuEqBm4qhYhWAhKntwelshZlZAP2xlVYPf2oVSMIzT+BwIkf2afj5JpZnI5WZM70Qkn0nYl9QtNJ3PZFxCFzaPnBzyiaMR29cSK0b6Pqhw8A4PT3Yq54A7t1O0II9MZJ+GYf5dX95hH71wskXl5A1aFj0U64yEsDBOz+XjZ88dO0tPeg+gNoroOmCjQBmgBU1VNKdQOiZUQOO5LK406gaFTDv6UcSCnJrltN/F/Pk964gXRnByk0uvriTP3sJVRPm0rin09T/qVrEZpO/KV/klqyGCcRRwkEvaAWw8AYM55tf32SmpEV2Ok0bc8vIKb6OPaFF0muXIFrmt4jm8XN5XBNk8SqlVSefQ7/vOgCZl1/A42Xfh7wAibG3nLrsPva++D9qEXFlJy5b+f0PwkpJZltTSRWrCC5di1uJk3F6WcQ3UudcgEFFLB3HFBkN9VLxftEdruFwnWhsgLZPZCxPy7G7EPfQsb2QnaleFtZwqBTwqAPrrJToREKUjVANbzYWs0Pmh+p+0APIPUgGAHQAnsQaCmlR4ot07t1bZlg5dhZ3PvBhQTcdAq7pxtrx1ZkXwcEi8Ay0RI9OI4JQqF89ijcktGYa9ZBzVhEpAw3m0Mm4whVoAR9KDKLbNuGotrk0gqurxgt04Ee8ZOzPI4r8yq8cPLq+2BDoFAAzZucuK5XmqCpaJoCJSVI2/XU3LLROOuXeuSuZiyyewuU1uHE+rATFpqbQCkqRcZj3gQnFMFNJnAyWUKnfRSsLNa29aSb2pE5E9/cE9GrR6CUliF8frBySNNrSLO7OpCWl0ImLQvXzCAUFWlZ3phsBtfMem4MjouS6iMyoZaON7bippIUHXMCuTdfofTKb+CfeajXnJZOIzNpnGSC3IbVyIFuij/7ld18aM2WHXTe83P0TBcVV9+CVu05CkjbIvaXh0HX0Sqr0Mqr0CqrUItL9ghb+E/D6mgj9cqLZDdvoKm1h/SOZiYcMg17y3pKLrqU8OFzUYujOMkE8QV/x9y8ESVShBqOkGppId7WRdW4kWTa2tnx9LOICdOYu5fmVyklTd+7k97XF9O0YQPnrVkHwMDiRfQ++zSjrv3asLZi8QXPk92ykYpLvvS+H4/3AiklG7/2VcbfedcBsT8FFPBBQoHsfvBRcDvfB1g72nD6eodZI1EUAariKVf5wAcxFPSQX6eqCE1FaLq3VNVdntN2Uxm9rIg8gVZVz69XVb0vqqH0NM+vd2isqnsPJR9DrOhDkcRCqMPs94EGiaiYASOnIYTiEc8tS5GtG7Adm8xzf8NKxNn88CKqj+5HqRyNku1DMUBVXEQol28KNBG+AE55GW5XO4qbxRg/g+QLTWghA19pOerkIyBUDOEoSlE5IhyFolKEdMi9+jfMRS8gB/qRKuALomQTWDkHra8PW2jYfRnU5u34zvsc1rLF5Lat9/x4N61ArRqFr6KIXKeDuW4dWjiE3jgZ2dmCOnocNG0k9exf0UeOxjdhEr7GCTjlo8i99Cy5dBx3bc5zXFBVb7KjayjB8NDER/iD+MaMR6+p8wIodouU9sa0X/Upcm0d1F11BRuvvIriQBDfrLkM/PYn+I/4ECIQRAmGUAJBRCCIMXoc6Vdb6PjyxUQ+djGhOfMRuoGvrp6Rt3yX7JaNtH/nepTq0VRccgVGdQ3R8z75zmfTdTFbmkmvWU120wakZe174l/+trwaDBKcPJXglGno1bVEz72QXMt2lIfvR/nc51l+9z1Mu/Qakk88SHbzRqSZRY0UE553DNEzP4rT30f/Xx9BScYIVFXRv72N0rGjqD3xRJqe/TsLTzoRNRhEyUd6K6qK0HVCI0cy/vobsOMxUi/9C8c0UX0+okfOIdjYSNMdt1H5kXMpPmz3soWi405Cr66l465bqfry17w68f8ihBBUfew8Ov70CDWfOP+/ui8FFFDAv49Cze6/h4Kyuw9I3n4JMhbby9p8CtcgBgnoLhePdPMevI43K3Nd7zVDKV6qQFEVz0ZLzSepKcKLB8wrxEITeYKsDRFl1F3KGAadGgZdHobS294DDgC/XqEqnvetPwDFFYhgGOkLwEAvMpcis2IT5NIMNHUSaKghl/GUdFsNIkOlyGAxbiKG295MNGISnDYVpW0TmaSG79AjSD//FOFDpqEGw543rW0hHdurz3XzJQyajto4C+WYj3rk9/mHyC5agLBshE/zDndRMVZ0DM6qxajFxSjhCCJUjEh2wujZOE2r0WpGoU+fTeb5JzA3b0ENBFE0BVFejWKlcCyJzGYJHHo4amkUdcYxOBtXgj+IPveUIXsyKeU+/xNKvvAMmT/8hOhHz6F34RL6Fi+j/ubvkH76Ee+zGn4IhlECIUS4CCUQIjTvWFAEqWf/gvSHEYpKcN6x+CZORQiBlJLcgkfoW/QWthYieuwJANj9/dj9fdixAdx0mvxOgxD46kcSmDSFwLgJQ01gUkrvrkQmgcwkIJOAdAKZTUI2vUvqIF64ysQjkdEa0mvXkF6zCqunG4DgpMkUHTGH3nt/TsmlV/HW3ffgCwYZIbJD5RiOaeKkU4hQhNDM2ZitO8i8uYSkXkRAZglWV9G/ajVOKk1oxiyMxnEoJWWIomIcx2Xj/b+lTNcpP3o+r193DaVHzGH+o38eOs5SStp/91vsRIL6L12xWxkIgNXZTtdvfkrlF76CXlG5T+fw/cDGr9/A2G/cVLA/K6CAfcCBpOx+P933viq7Xw2WHpTKboHs7gOceC9Y6eFXagbC8HvEKZNCphPIdAqZSSFTifwjhkx5X+gyX4ogrZyXnmbnvNc6jue8MJiWNkhUB2OF80qXdCXScTyy7A5zCoflRgf2rE3mrdYkKkp5BUaJHyE0HFdDM3vRxoxF4JJeugaZy5JJ59BqGxB1k8itXw6pAbCzGKVh/KVh4hvbMBM5KiZU4STTMOkQ5PrVONksorzG86fVfd65E/kku2waNRLE0L1wCwkIw49y6HFYS1/A3rwN13bRi3yIaBT/BVch3/o7smYC1oo3MXc0Qy6DWtOA1d6GVlyBUV+DFg2TXb+RXGuHd/4dG7WyElCQ/f0Y9XX4jj4B0jFE9RicLevQjjgRdcTodztswx9Lx6H9U6dQfNhU3PrpdN39KwKnfYySo+bhpFOQSmC3bMVNxL1rzhfAGhhALSlHDYfQwiFCZ55P+rUXya56i8CswwnOOw4hBO7613F6WknlilAMA62kxAuniEYR/gDCykKiDznQ7ZX9pN42QRTCSxkMhCFQBIEwIhCBQBj8oSGSDx6ZdF9+FGXC4Yiqht02E/vXC8Rff42qT3+O/vt/Rdkll9O5aQsbHn2UksZGRs0/moC0MTetw+5ox9y6GVFWgVJcQnbDWvqzCuWVEVSfQeTD5xN7ZSHpNatxk3FUTSHUMAq1spL167Yx7ZxzWP+NG9m4ZjUn//Tn1Jx/wW77klq3ltb77mXkVV/BX1+/2zo3nabjJ3cSPfVsAlNn7EGI9yfSTVvp/ttTjLryy/+1fSiggA8aDiSy+4Ns//tKdq/xlxTI7oGM/XExWs/9EuLD1OwOBkW4cigxbWet7i7LIXVO5G2tdISmg2qApnm3oFWv9EAKgUDxxFnHRVo2WDbStpC5fLywZSGFeNe4YDloXea6nt+vY+eDLAbtzNzBD/FfLv2VyJyNGx8A0/QU7WAArTyKncniJrJoZcVoDWPJ/Gsh0nZQqktQQ0GMijJkuAzX1nD747h9vWhajHRrjI63tlF32AhyfZLQCSdgr3wdoesgbYR0vQS1vBIvfAauUYSVU3ATCXByaGEfmhlHPew4RG8z2TUbsPqSHhk3dBzHRboCraIKbUwj9vYN5AJjMAIW0nbJNbWiR0PopSEoH4G1ahWuK7B3bMVB8eqGDT+6IdFGjcOYdyKitwXpSPCH0eefjTD2tMaS2TTovr2Sp76f3YFc8xpF555P7LWl9L22hBF3/ZzQ1Om7jXOzGcw3F5Fd+ipK3Rj8U2YSe+R+pJmh7CvfRKuoIvPm66ReWYB/+mxC80+Arm24qxaCfxhrLMOPiJR5yny0AukLY7ftILdpPXbXPlqPCYFv6kz0rrWo049BlO/+t53r7KD95z+i7KPnkf7HU0Q/8RmMupEkWlpo+vvfiW3dilBVao44grojj6T3+7dAaSUyk8GOD9DTnaR+xgREKIy0cij+AIEZhyBKKom/uZSeP/6OyKWXk02laPjQMTw8cTwTjz6GquOOo+ErV++2L042S/MP/x+RWYdQfsqpu58r1yX2zBOY27YONaEK3cAYNRp/43h8DWO9a3I/YMt3bqP+85dhVPzvku0KKOD/Cg4ksvtDc+B9JbtX+6IFsnsgY39cjLm//gC3u22YNYO5wIOkVtml238XD92hMoGd3rneLfTBEIq8dVk+aU26LuB6t7CHHni1vWJnre7BVGcjggZqOIyMlCGTaZzWZtzOLpSQiis0XF8Yu62D0KnnkH3iIaQviFrTgDPQD8kB3GwWoQqEAnpYR2kYTS5l0/PMAsK15SjRMopP+7B3fsgfYzs/ichmcHq6cXs7UKwkiqGghCPYkVrMLVvQsr2oVSPQK0qwtzVhdg2g+jQUzTv+Ts7xJiWqgmIY2BhIVxKYfQi2W4S9chG+qmL02fOxVizBiqeRsb68Qm+TtQ1Uchgyi21KjDGj8TU25i3QLOzebtzuDtxUyiu5cHeGmUjpzV1sPYIoryV48ocJTjuErkvPpvTsU8hkAsSe/gvqESd4E6y3Q0oU6aDZCfyTZxE56+OkFj5PasGzqFV1RE47B2PUGDLLl5Ba+E/802YROuakoWYn18zixmM4sQHcWD+55m04PfmJoSLQ6xowxk1Ar9q3yG/pumSXLyG7ejm0bSB09ifxzZ632zUvHYfOe3+NEgwiejuInHIm/glThta7tk37G2/Q9OyzhIoj1DppHAm5znaIlDLQ3MKIQ6ZgjBqD3jAWN5kgu3YVbjZD7+IlVH/kHNa9tZaZX/4yb3z0HHKVlVSHwjjpFFN/fTd6aelu+9zxp4exYzFGfO7z7/i36eZMctubyG7eiLlti3eeAaNuJIEp0/GNGTdUg/2fhNXXR/PPf8bYb9707oMLKKCAAtk9CFAgu/sAe/UCSPQMv1Lk1dr8l5tAAvkoYJknsEiElLskqnkj2WUB+ZpGOdisJvLBChoohtd0pmhIRQehIxQtH7ZxcBBet3UL7paVyFwGoYEIhBC1Dcgd65CJFMIIIupGklqynMDoWqyt2z2FPBRA8flxhYBMGkVxsTr78U8YA0iobKDj9w+hB4sITJqIVjVi54RkcHKiqqiGgUj3gplCpjM4iThk0+gRFSYdhrtqCWZ/EiUSQVNd8BeTbe3ATSbRgjpq0IeiyHztsYrtGtgIhG0TmHci2Tdfh1QM46gTUZLd5LZuB+nixmOgCJRwBKW+EYGNvX0r5o42VC1ffq3oaKMaCZzyEXwzDsdp3oLw+VFrR2EnY9gLHsNZtxSZGMDs7qfsnufp/PyHMUqL8R86h2xLJ9ntzVRcfxtaVc0eRMxs3k7P7+8ls3IpWmUtFZ+/EiXZhxsbwM6Y2O0tHultGEtmxTLSi14aUiOF4UMtKkYpiqIWR9HrG1DLK3cGwtgWueYmnK7OfboePPcRG2nlcOIDZBc8ju3qCJ8fo6ER35QZ+CdNQy0tJ7XiTfqee4bI+EbcgT5vv1QVfWQDvnGTMEaOpvmll9jyu/uYdvJx5Jq2kt28EcZOIRVLEqqpJlRegmamvJkDEH9zGS4K9d//GW/cdhvjj5rLc1/6Ehe3tdP6xz/Set89jLryKqo+fM5u+z3w6iv0LfgnDdd/HWUfFFspJVZLM5k1K8lu2fSud22G2QBSughNxz9uAoHJ09Br6/Y4182//DllJ5xIaNz4977tAgr4P4oDiez+KBejgveJ7KLwFaO4QHYPZOwXsvvy72CgY5g1+bpZNx8B7Lo7f3acnaESjuMN302p3UUN3m0JQ84Mg0ET0t359C6BEgeVshswEEUlSH8IN9aP296J29uPNHNoER+5FGiGglo/glxMIro2ecq5quWDFyaizjgGKurJfPsihBCoE6ej6OBsXEdscw8D23uQirJLzLLMG2BIT6lUPfIrDcMbkzMhm8EfNjBtlWBpCNfOoQVD+EI+8PlxHYmTzSAtz5dXWhZCVyluqMS2QKusITeQQh89Ec3sIdfZD6EipJVFEQrCMnGNgJeGlkl57gqRKGokiO/EjyEBq2kD7uZ1uD0duOmU586gqp4yLPGaFvPhE/brz6J+9EpEvJfEn39P+cfOJpXxk/nn31CrRyB9QZTiUs/BQdfR60cRnn8Cij9AdtlrpBb+EzNj4eoBnI4dlJ56GuH5J5N49q9YLc1ETv0wxphxe5w/ads4iRjW9iZym9Z5ijuAqmKMHI1WXcO+TsyEpnn7aRigCOTiJxGHnER20ybMDauxmpu88AXXQfiDmF1dnvewqmLUj8JXPwph6NhtrYSPPYlcaRWvfO7THHHd/5Bb/BKpNSup+MwXUGrqGVi9huTGjQAY5eUoW1aTXLOOKX9+gh2vL8FOJln25Ss5/pnnqJg5k9SmTWz42lfx14+k8eZbMMrKhvY7vXULLb/8BaO//k30aPTf+Gv49+HmcpibN5JZtxqrbQfSsig++QwCU7wSFtc02XzLTYy/4879ul8FFPBBRIHsfvBRILv7AGfxvV6IwLDY9TCKPb12d7UMUwYV4F3cDwadE+Qu5Pbt7zC4bpA4DxFrORRDvHvssLrzvT4IhFiC7O1C9nUhfEGUsVMR/ggy2YO94hXkQBz5/9k77zi5qvr9v8+t03Zne8+md0hIKEG6gDQBDYKAgFTBTrOiIqigfMGGipQvLYggIEUQRZoQIBBagPS+SbaX2Z0+t5zz++PObjYQIFEC4fvb5/Wa3J25M+eeW2bynM99zvMIA6KV4OYxK6KIvY5DjJ+N6O9EdqyGtlWQSUCmH+nmyS9ZiWgYjxk3EOh4LWuRDbsh4lUMDiSEkgHB1Q0QCj+bCXxrM1mU0BChCOb4iWgrn8Nbt4ZMn4tZVU563Qak72IffDzhhgbMnrfQBeh7HIn377vIvrkKt+AQqS3Hd326V3QQHtVEZFQzYqADvX4sejSCUj7exk2Q7kcLhSAURuYL4OSCwAzPCc6pUkHB3/eDwRQq0DUXz70SemAIIhUiO0DBs6i98a90nnU00WkToGY84SM/T/6Fp3E3rEGlkxgVVegVlfj5AvmuHkqPmkt4xmzyLz1D/rUXMSZMxZ69D60/+hZGXQP13/oBmmGS+udDuK0bAgIKmwcOuoFeGsccNQZr4hSM8sqtn+v/5jJx8sin70Q74MRgktvg61Libmoh/9bruBvXB10KhXF6enA3tiAHEmga1P3w51BZwxPHHsmM734fnnsK6Tp43V0oxwkq7OEIZvM42l56FauvgzHnX0D5SWfw3Pe+h7lsMW19/cyd/xwAXibDqh9egtffT+1nj6PmM58Z6pPbn2DdFT9l1Fe/TnjsuA/8WGwrlJS0/+Iy6r9/+dDguOO+e7EbGijfZ9+PrF8jGMHHATsT2f2tm9yhZPd8s3SE7O7M+FAqu3d/G/FeF9nbDqUatAEbIqmDFVoIiJbY7K0r9GFa3EEPXTYTVU0rTl4LJrahm2DYYJlBUpUZDnxelV7UAw9WkweDEd5jxwbbMuyg7Y9MEhEcH+HmUJluZNtGVC6PVlUNhsJdsgzNEjh9PlZTLUiFZmqIcBSFiXQVUpkQLkVJhda+GJwEhdZ+rL0OwPAT+OvXo40diz568uZ9tmOgmShP4m/aiN/dEZwj2wJdQxZyeC1rUCX1hMt1aF1Jrj2N3dyIZvpo+RTUjEY0jYWojYjWo1qWo2SSzL9fJNveR8n4enTdIJ9zSHbk0Uqi2JqH7xsYQhL71BGYFaX4WYXz0r+RuSTCsJGZFCqfD7yZQxbCMFBCw8/kgkAJr+jgoQmErqGZOpqhITNZEBqx7/+O9A1X4KayxPfdA/0Tn8WaMAUAmc/jLHmNwrI3g4qyk4OqUfi5LOWnnkNh4bMUFr+OOXE60UM+Td+Nvya1dgPhGbOoOuHkbb49LwsFciuXk122BKd9a5r394ZZUYnV0Dj00EvjkM8gX/kH+v4nvPvV5Lnk33yN3KKXUY6D0TCKgddew3vteRp/eSNaWTnPn3sWlUd+hsbKEpRTQBWT0/zkAM6G9fQveJ5CMk1swgQm//kBXF/y8rcuZu3fH+ELLRsxQ6FgW0qx6ZabSb6xCCMcYfQ3vkmo+DskHYd1P/8Z8TmfoOwT+2DE49t9DD4IpF54FpXPU3rwYUN9XvHti5l89S//T90dGsEIPmiMkN2PP0bI7nbAf/QaSG5Fszt0CCVD0gMxrNYrhh1iIQLd7nAv3C3aeGfbanD98M/IYe4Jg9ZjYivtDMp5h29GEBDtQacIAKGhhqe8fSQoVipNG+L1EC0BW0Eqi1y7AlEewV3dht5YibO6h9Dk8Ugrioo0oMXj6CVhUAVIJ1CJLrzVy9CrynFXr6GQM4kesBesW4Kb8oMBwqCDhipOAtQ1tFgUvbEBrSyOymTx2ruRyTSipBSla6TfWEuoOoYdkTi9KYKkNQcND6UHRFT5HiIUC8YQu87AeeMtUkvXYMbDhMtLwbaRRjlGmY2M1pDYlEAtfS2o0JoGVnUV5tjJeOtWovp6Nlf5dQNhhzBHT8DeY39k3Shk3sHr68Zbsxx34zr89o3IZD+xmEJoOm7dFEpnz6T/kYcpnTker2Iipaec9w5yozyPgdt+h8gksQ/6NMl/PULpsScg21pwVi/DHD+NyCePIHXn9YjmSSSeeYbwhIkY5RXvPItS4mzaGOidAWFZhCdNITJ1OlZj03ZrUL2+XgptrTitm3DaW/GTQbv++iXEDjmGssOODiri7wOnZS3JB+7C9RTZp/5Bzbd+hB4vZ/UD95MU1hauFnZZGZHqasK5ND03/RErn6b6+M+jRWP0+4LWW26hzQwz97HHiA2LMe9f+BKtf/oTRihEeOxYmr50LpoR6OoTTz9JeslivGRqaN+s2lqik6dQMnt3jFjs7V3+wNH280up//alQxPfEs8/R37TJupPPGmHb3sEI/i4Ymciu9f6O5bsflMfIbs7NT4UsvvK3yCxdc1uYB9mFauuBuIdAQ2qaAHmB9XWocegDZgEFQQb4LtsZqfFf7RBz923P0Chija8wwjwcDnEYAzu+51pwUcfLGEaQVSy5wFySLahfB/VM4AIW3ibutF2mYH76hLsqRMQkVhw/EIxlAqqnrK7C0MbwCeCkesgtbwLa9eZ2GUG9LUjIqWBxGOY9ZvSTKRRgkwkUNk0WCH0UePRZ+6Ht2wh8s35WFMnkV6Twlv8IpFRFeilUfyMg2eUoee6kekM0lMI3UAPGQhDoMoq0CKl5F59nUxPgrLRtWglcShvgFQf+pRZGLvMwVv6Cq6jUVixhPymFlxhIpomEaqtxXUlMp0KshpySfR0LyYumm2jxcsD/9jSMvRwGC0cInvndeBJpGZTdulvGPjVpWgRm8i+B+Kmg8lXQtMxmkZjjp+MUT8KhCD555tQ3a1Ej/0CmVdfRuZz2BVxvO5OjDGTiOx/CMnbf0/sM1/ATaU3h0i8DVbjqC0qmH5/H976Vfi93dt3PQhBaM/90UreGckr0/2k/3ozqT4f5TrEZu9J6f4HDYVXbA1KKdL/eoTUvx8n+9Yi4kcdi15ahmaHCM2YhT1uIhgmhYEBsl1dvP773xNfuwLZsobqOXsQP/o4jMpqFn/7IpTjsiaV59A/3UnDnDmb+1Uo0PKH31Po6ECBidnFAAAgAElEQVQ5Beq/cAple83Zal+crk4yy5eRevUV/GwWYVqU7rEH8Tmf2CHkN7f0LXLLl1Jx3IlDr2288QaErtN41tkjFd4RjGArGCG7H3+MkN3tQOq67+G1bXrnikGf3bdVaYVpoFkmwrbQLAthmWi2hbDtIHErVhqY6VsRsIsPKwp2GGFYILSie0PRG1cOBk4ULcukj/IdKOQgOwBuHjwXfKfoQ+UE5HqoMjgomRjs9LAvzND/cbLoBPERwclCIR8Q0cpREK+FnhZUIQHCQLZ2oJQIJv3VjMFfuxHKa1C5fFA1j5WijxqHXlWJ9+x9IH2M8hK8jZvIpyB00EGYngMl8eLEwc3HSzg5VLIf8plALhCNI0UIv68fYZnoBx6L88hd2E1x1LQjSd12LWSTEIogNA1hR8HQ0XK9SNdDqjB2RKJXVODlcmj14/BWLyff1Y2m6/iuhHgFpmUR3m02oaO/iL/0ZVQygZgwC3fxq/hr3sL3JZou0IpERCuvxJgwDX3CriB0/LYNeBvXBo4OKpi97y1egNvegVQa+kFzMbtWkW3roWx6E/ZRX0Q0BVIGb1MLztoVeBvWodfUEz1iLumH/4K/ZinRI45DhUtJ3DOPUEMtoGE2jyM85wCS835HyUlfCkg7AXFThTwqNYBMDeC1bcBr2xAMQgAtXo45ZgJaVe12ESrlumSf/jtG0xjCBxz+js/68+9F2/PTYIVIv/YKyfn/RrkO4anTiR9w8LtKBvxEH53/82Oyi98gtsfe2FOnIxMJvL4ehG4gbBuzsZnW7n7UulUk/v4oDfvsSd2Xz2fgsUeoPPUs3rrke+QWPM9GqbPLpT9mxplnbrGN7Jo1rP/D7zHCITTDZPT5F7zDpuztkIUCyVdfYeDFF/AzxUmI21kJ10I2sV12JTZjJnZd/Tve0n7Nz6j92sVow1LUEs/Np+ef/2DcJT9Ej0S2fXsjGMH/B9ipyK5MUrODDPG7EHxTGyG7OzU+lMrus3dA4l18dgerqYOThQhu5yrXRzpBEIQsuIEmMJ9D5R1kIb+ZjA7qebeI+Q0gDB3N1BGmibACAq2FrCCtKhQO/tMKl0CsEkqqoLQaLRwLSLRZJNFCBBGthSw4mUCfmc8gc8mAsOXTm6vNHxkUZBJBVLCmYKA7IEtNu0C6DZXpQ8XLUMvW4Odd9NFNUD4GrXkamuGjsilkTxdqYADlSdw1q7BiHn7ZeIzepaRX9CFqa4l9+YfoJRXg5FFOPjgubgGVS6N6NgUOGviIdAIS7ZBP4YkYSg9hHnQghX89gRkuoI2fiexLQqYX+jrAiqAKOZSmI5wMbjJPviuDaSusqjII2eRTCtMvgJdDiOD05wuKQkc/mBZaWQVa0wRUb3sQldswDhwHEY0hDCu4O+A6qFyQxCdCYUS8ags/ViUlhX8/QmmlTWEgS0GPUXHg/mRWrMaePhPNSaI7ATFWpTXoE3bHGDMRv6eTwhsvU3ryl8jOfxznjZeI7P8pzKm70f3HX2GFDLTqWsyG0YRm7036vtsQw+QDwg6hxeJoJaXoDc0YDaMCsjbYL9fFT/Ru3yUhBHpVDe6Kt8g9/yTRw+diNI3Z3GayF7nsBfQ5x2x+TSlySxczMP/f+MkkRnkF8YMOITRh4jvIcvcfrib5z0cwJ01D0/WhgaDyfCjkyK9fy6baCYRWrcTOJJh+zwOYTaPpmfe/6CUlFKLlrDn/q3S6PhWfO5FP/vrXW3oAK0Xn/ffT9+wzmKWlwXkSQeR3eOxYopOnEJ08+QOt4vrZLOkli0m/sYhCZ2D1JgyDhi+egV1fj7OxheRTj1F1+rlbfC7f1sb6q6+i+RvfJDJu/AfWnxGM4OOOEbL78ccI2d0OqM6lqGzf1tZs/vMdMgU3qLbK4gx6NTj5TNtiqYqeukK3wAoNTRZTuonyQboOMpeHTAo/PYBK9iNT/UNRxLgBYRuSRbxd0jCIoQlvRacGvZjaZoXBjgbLj9CzV9lRGOiBRCvCtjBH1WGZLkIoKAlBzkHFIsgV65EOaGPGQrgWrBBa2ELgozL9+F1tqHwe8lmQEiMsKLT2IrUYfkUtbG1yVVEyoZeXYY1uRpc5SCaC89fZggzXIAljH3cK+UfuQEt1Y87cA1Feh8pkUaNmwfq3UKtfQ3WsRuZdZHkj2TfeQtMNQjVR9IoK3KyLP5BFc7MIQwustcZNRq8chdcbhGOgaZj7fRox0IXKDATnCFCug8xkA/mAAuV7qFQikH0YOnpVA1rzRPruvg2zkEDmHbL9OcIHHkHYcHEG0lRd/luEpqOUwl+7GH/x88iuVrxIHeFDPkvqgTspPfkcnJVLyM9/nNDuexPa/3B6b/kDdG7AmrQLRmMzkX0Pftfz6Cf7cdeuxFm7MogkBoRpopdXblelUnkeXusG7GkzCO11ALnHH0Q5BaKfPhFRlCv4z9yNts/cQOu9Fbi9PQw88xTZxW9RdujhlO6z3xbr++64icLa1Zh19egVlYFkJpdD5nPkl7zBa48+wdjGKgY29TD1+9+h6twgaje3dDGJ+++m9DPH88bZZ9K3cSP5aTOY+49/Ygy6VBThpVJsuvUW3L4+EMGdCc0MUhNVoQAErholM2dSvt/+71sB3l546TQt11zF+Mt+CkDn739J5alnYpRtuR3puqy76heUzp5N9VGf/kD7MIIRfFyxM5Hd35HaoWT3G5SMkN2dGR/GxSgXPwLpbdUcDvPPHfr77e/ZrLtFyaKutmgppuTblsXXh1/kg3KD4bIDXQMt8F8NgiiG/60jpET5blDNLFY08Zyi/KHo3PBRwvMCa7Hpn0T1deG+Ph+3dwBdZdCqKrBKTKQVhlwW2dqHREdUVKOyGVShEMT/Fr1j/d4uZLgGS0sE1d2BlWTW9BM5/hzM3Q8MBhWm/Y5qn9fdSX7RQrzWDQDo9U1Y+bVoa99Alo1BSgP7zEtwn70f77m/IRomgm6DbiJigdWWdAvoLc+B46EappJ58Tl0O4IZBaOsDNE8Gpkq4C5fhqaroNpXWoU2Yz9ENoE2cTbeWwtRPW1QXht4/RayYBiBRjdaGpxT3wsuL00PKsqahkwlKbz1OgNvLiZWE0N6Gh0bkjTstxuUN+JtWINRU0fs2JOwp+8GBKTZuftqHKuW6KdPJPmn64kcejQqlyP7xN8ITZxC6MgTGHjoHpwFT2DvuT96rAQtVorX1YFMDWxBYrWSOObYiRiVVfjdHXgtq4PBx1a/B+8O5bpYk6YjoqVkX3gas3kc9q6zyD32AKF9D8GatAuqrx25/i302Ye9b3vdd90BQPXJp23xut+fIL9iKYWVS5DpdLAPsRIie+3L0mt+gXAL8PICrNISmn7xO0oP+CRCCKTj0HvH/4Jt0/HCS7Q99ABtZpjPL3yFaE3Nu++XUjidnaSXLyO9dClOZyfS99HD4aJUSQWBGFWVbM8BE5pGZMIESmbuhlVVtcW6tttuoXSvOcSmTcfr66X37nnUfvXCrbbTce89ZFatonyffTCrqrGqqjCrqtB2QKLbCEaws2OE7G6G4zhcdtllLFiwgP7+fkaPHs2FF17IgQceCMC9997LjTfeSE9PD7Nnz+bKK6+ktrYWCH73rrnmGu677z4APve5z/Htb3/7Q5krMEJ2txGrV6/muTt/jTOwtcouGIZOyLawTZOQZWKbBrZlELJNbNMiFLKxLQM7FCYUjmCHIlihUKDNNawiKbWKFdfAU1UMVoOLDyW9LTdaDJoY1OQOkWXPBc8LbNKGjCKC9ylNL6aw6cMusOJS04KK8kc1SUWBMhSseglyaSivQ4zfI5CDLHuK/IoNSKETGtOIFtZRiX5kzoRQGK28Fq1pAtqoaVDZiFKKwv+ciSqpQHMyKLsEU/aTbxvAV2GEHQZU0e44OOZC09AqazF22QNj10+gxStRSuG1biD7zGP4HasI5zeiVY1FaiFC51yKfPinMP4TiFgZ9HagzQ5up6tCnvyjdyIXPY4esZDhCrIr12LWNCESG9EjFlTUoY/dFfflp8AtoJkGmDbKsJFWKdL30RomgJdHmKFAjlKUyWiRKCJaUpSnFALbrEIOlUxAbgC5bgm5AQfSvQilIyIldK1qZfwfbiY0fTec5YtJP3QXXkcrWrwce499iR18BM5ff4NXNoHo0SeSuv8OjPpRCNvGXf4mRsgmfNzpZF96nvRdN1B6+tfRyyrQq2pRbgG/fSNe+yZkX08wKdJ3kY6H7wZLYVr/keez7NyESibQxk4JIonbNqDX1GFWVhA79BisidPwB3139fcnY8nn55Nc8BwN53/rPe3T/HSK3ht/i9E0mjeeeYGStSsx29dRNms20vOpPO8CSvcLfuBzyxaT+OvduHaUdTf/L2u6ejj07nsYfdjh27yfSikyK1fS9+wz5DduREmJ0Lb3eAl0OxQMaKUEoaHZNrHp0yk/4EBarrqSCVf8AoDuW64nfvjRgUPGVpBvayO7ZjVuTw9Odzdub29gdQfv3ielGPeDHw7FSI9gBP8XsDOR3T+QpkbsILKrBF8j9p77mc1mufnmm5k7dy4NDQ0888wzXHTRRTz88MO0tbVx/vnnM2/ePEaPHs0VV1zBmjVr+NOf/gTA3Xffza233srtt9+OEIIzzzyT0047jZNPPnmH7M9wjJDdbcAtt9zC2Wef/YG2OQjb1LENg5CpY5vB0jI3P7dNIyDPVpE8W1awNA1s08S2dGzLwjItLNvCtEJYoVCwtC0sw8DUBZYusDQwNbB0sDSwdB3T0LFMHcswMHSBqQnERyhjiNo2enkDRMOw5HlwHahohLAFrSsppHzya1uJ7jUVPZ9FjN4Nbd9TIdGK6tmASvcF+uPuDUjAffNNZOU4LLcNacTQZRI3JdFrGyAcRYQjCDOCMkNIzcJr78Rv34TIJ9FsC62+GXPmJzBm7ocqFEjdfS3ey/8mNKoJUV6HfdChsP4lxNg9IJeHmUciijpsYYdxXvkHzgO3B+QWRa6vgFEagkwaM2KiSqrwtShq02qU46CHzUCzrOtQWoZRXo2oH4swTVQug0p0BbKFbCqYpCgEIhJDlNciKuuHbuXnn3wQLysZWL6aaHUpQrcoxGrJ9PRjz9o7ICvFr76f6MVfs5RIbQ31V/4W577f4NXPJHbEXLLPPYkc6AuIqpLQ007khHMorF5O/3W/QIQDPbiwwxAtQURKwLIRSqGFwlgTJmNPmopRU7fdo3clfVQ+hxaJ4fd2kX7gT4QPPRa9qpb80jfpv/2PKM+l/JRziEyeiOpsQZtx4Da1nV+/ls6bb6Dxou9u1T5t6H3LF1NYuZyXrr+BXU88iWWXXMKkow4ieuiRJJ9+HGvCVOou/sGwKu/N5FrW0fn8iyx97XXGnvB5Drjx5u3a76H9VyoIudiez/g+mRUrSL6xiNz69aBUUDG2LKRTID5rFqGmUcTn7I3MZum6/rfUXfT9/6h/W0N6yRI6H7g/ILwjzg4j+D+CEbL73jjmmGP4+te/zqJFi8jn8/z4xz8GoLOzkwMOOIDHH3+c5uZmTjrpJObOncuJJwZuMPfeey/33nsv99xzzw7Zn+EYuSf1PpBS8r3vfW+HtV9wfQquTzK3wzbxsULINNh/SjOH7zWdww4/jKmj44h1iyEFRKLYegFj90kkFywjuuckjFULoa8dNdCDcgso6eGl83gDWayKCCISQpOpIHCspByRTWE0NiAxUAN9qI7WzYlkUqLpGkbMRlSVIHMe7tpluItfQbv3j0R+dDPxsy/BO+iTZG68Cr+nH3f9KgQecv5bSM2G+/8GoRIQEJ65B6GjTkRzu3BeWIjs6yNcYeH6AtcTyIE8luzG3nMq7L4X7oIn8fv6UT39iLCJns/h9fVA16Zgop5TdNkoklTl+wFpTfTDpk1BgV4rWt+FbNTAACXTJpDfsAEjYmJbgvDkUURmTcHa+5AhJwUA6ftsvOBs1p9zMvU//jnGgvvJPGUTPfgoci88hSzk8TvbsabvTubu64l87izqfv/nbT6vSilkMoHs60GmB1DpJDKVRGWSm32ig3du/lNoCNNEDiQIHXE8pWeeT+bv9yBCYSKf+gzhaTNI/O+vST70F9JllZSMqya8y/7bVFUMjRlH47d/QOsvf07NKWcQnjR56++bsgupf/6Nxl2mkTJstGiMzMaNVE6citfRhvR8Ws45iabf3IhREqf67K+QX7OqGC8dYelDD3LPc/OZu2AhZnz7IoOFEEO65O1B6axZlM6aNfRc+T7pJUtY/ZPLqD7q03Q9eD+le81Bi0SwRjWTW7GU8ORp272drSE2fTpOXy8br7+O5q987QNpcwQjGMFmCE3ssJuvQgm219Wsp6eH9evXM2HCBF5//XW2Vj9duXIlzc3NrFq1iilTpgy9PmXKFFatWvXfdnubMFLZfR+4rkt5eTmZTOYDa3ME245RlSUctt8cDm8yOeqI/Qn1tkFZOSqXJrWqB4FE5At4nkKEwwjLwqgpx6wpI/PUi4QnjkX19EB5M6bsRnkSvaEJUTe+KB8pxitLP9Au93chezuRuWxQ4TYMtHCYQl8Gf9NGrLN+RGiX3fHXLsR/5Hpk02zUhkUgReB8oRuI0TMglyXf2onX2UbpWeejtzyFnzcozP83eshAi9vk+xz83n7seAhztz3Qx05H+QJv4RP4fX3IVBqhSzRDB7TApMOXKE8Wk/QsBAolAweNQJIRyDFkJoMKRUEY9L+xFLs8jmHbFESIyJ57o9sh9EgEa7e9MSbtihAC5XkkfnMZyaUrMafPoDyahV0PIbLvIWQeexBRXonz5itED/sMhacfJnLsKYiySlQ+i0onUakkMp0MiGyiG5XfcgQnSsvRyqvQSuKIWGmwjJZsEeawNahCntxjf0WEo4QOPgZ3zTJyzz1ByfFngG7Qf8u1CENHeQ5edzclnz+L0PSZ21RZVL5P++9/g3QdhK4jNB1hWwjLQrND2KPHoCsfP5vhpeuuZ/SoRroefZTd5t1BZMZsErf/Ea2mnv6/3kXNxT8iNieI3lVK0XvPnXTceiMb17XSMTDAIT+7grqjj8Gsrf9Iqp7rr/sDqZcXUnfCCWiWReWhh6E8j/b/+QnxI48hOmvPD2xbnQ/cj/I86k74/AfW5ghG8FFhZ6rsXqdldmhl96syus376bouX/rSl2hubuYnP/kJCxYs4IILLuD2229nzJgxXHnlldxzzz1cc801HH300UydOpVHHnmE8eMDt5f169dz+OGHs3z58h3+mzhCdrcBF110Eb/+9a8/0DZHsP0YVVnC4z86kQm1cVQ4BKk8XrQELRJBr6xEhMogUgmFDCrVi+pdz8CjCwjVlCMicTSyqJJqDAqI8mqG4uWGHCoEQgwSLwWei0oN4PclQLrI8lG4y5cipu5L7PQLkOtfRT01DzVxT0TvWgjFUT3dIMzgFnR5M4RKSC94AYFHbPfxaPVTcJ58GL+nG7OuEqEr0q0DmF4Bc/IUzMpyxCFnIpc+j7fgn0gRwU8mEWYIEa9Aqx+FXlWD8FzI9CF1E6HbgUY21Yfq74JkH157K6KsgkJbHyJskl61AbuinJIvfh1lWhRefha3qxPluChfIqIlVHzj+xjlFQzc9EvcgiRfKBDzuwkd/kUi+xxE6r55mJOmkXv+SUq/cC6FZx4NtOHhcEBeY6WIaLDUyqsCiQMB8fO72nHXrcLv6dy+k65phPc5GL2sAm/jWgpP/x1r30+h1zWR+vMNxI4/AxEK03/zb9F0QaQuSr50As7KpUFC2YTJRPfaD72sfJs2pzwPOSw2OPXSAty+XvSBbhY99hR7X/0rFh52KJM+czijrpuHUorUPx7Ebd9E6sUXCE3blboLLxlqz+3rZfV5p5NYu45lrd3ololpGsTK4pTVVFM1aRIVn9iHqkOPwKyq3r5js52QrsuSr5xHbNo0vK5OJlx5FULTUErR/+A9OO1tVJ/55S38d/8bbLzpRiLjx1N58CEfSHsjGMFHhZ2J7P5Rz+5QsvsVP7JN+yml5OKLLyadTnPddddhFuc/3Hnnndx+++2kUinOOOMMbrzxRm644Qb22GMPdt99d2699VZmzJgBwOLFiznttNN4/fXXd8j+DMcI2d0GSCm5++67mf/3u3By76zwKqVwPZ+C41LwfPKOj+N65B2PguNRcD3yjhusd1zy+QKFQgHX/Sg9bT+emDOlmfnfOQatohwcB6XbaA0TkakE5HOQCyy50C3Qs5DLknx5DVYsjFZShhEWqFAlCAm+G0wCVJtdL5SUaJEIorYZUVEXTHjyPeSKBchUCk8rQXZ14OnlRM++GNH3Frz2PERMtEm7QMVUhBVB2aX4T8yDkipkwUdWNJL5663oFaVEZ8wANAovPI0eL8GsiOCUjsF7/SWU7+PlPaxJkwkfdx5i4QN4XR1DNmP4PrLg4hdc/FwB4Qa2bMIwwQ5DpBTsCKJtOSgN1wGreTR9zz2PWVGNFbaJnX4+5i67I6IlyPYW/KUL8do20T//ecov+DFmZSXp++fh6zbWhHFkH/4T9gFzic/9Ask7/khoj33IzX+C+DkXbOmjKyVyoA+vqwNvw1q8zrZAciEERk095tiJ6NV122c95jpkHnsQo6aeyCFHA1CY/xiypwP74GNI33c7sc99ERGJ0X/Tb9DdBNFPHoUx66DA8WDNSrIvPYef7EcLRyk74VS06PZ52g489wz999yBnDiN/p4+sg//jVhIMeW2v2BPCm7/OxvWMXDvHXgFl8LKZdRf8RvsYRO/Wq76GcnHHgEh8KUimXPoT2VJFhzcQgGBoqQsTsOk8Yw6+lhqjz0Os7Lq3br0H6P7n/+g66EHqfzkJxG6Tu3nThha53a00X3r9cQ/dRTRPfb+QLa39hc/p/rIoyiZOfMDaW8EI/goMEJ2t4RSiksuuYRNmzZx0003EXqXuPZ169Yxd+5cnnnmGeLxOCeddBLHHXccn/98cMfnvvvu45577vlQNLsjZHc7oDqWoLLvYYo/mMgVvHuzVZjyN1uHSa/oniCRUgVk2HE3k2HXI19wiiTZDQizJ8m7ioKnKPiKvKtwvOB53pMUPIXrSxzHxXEcHNfFc4t/Ow6O4+K6Lo5TwCkUcAp53KF1TnGdi+O6uJ6/Q47dtsDzfXKF95+Qc90ZB3Pu0XuhdA1yQLw+sPwqqQDdBz8DThq19FUoK0X1J8kub0MPRTHiJsbUvdHG7QaxMkS0LFgagS+qkhJ/wyq8V59GdW5AKYVRW49eUw2rFyB9Dac3C14e142glZYiYiXonYuhthmtaRyabmHsPReyfcilL4KbQzpAvBz3rRdxentRrkQYAqOQQiiBVRtD/8z50NOKu+Bx3I42nP4Uyoyh1TWiHAeV7EdIH2Hq6GEbIx5Dsyyk0IPJXAUXWchCNotM9KGHggl2XmsrKIfEynbCVRWYZWXoTaMxxk5Gj5ZgTJuF3tBM/p7rSL6xlNhnT8Gqrib38vP4BYfYp44k+ccfoxp2pfJr3yV567WE9j6I3NOPolUOr0YK9PIK9Oo6zFFjg0mA0sfb1IK7bhVu6waGqunbCiXRyyvRaxspvPoC0SPmYo4ej0wmyN4/j/BnTw0I79xT0UriJG74JVahHcob0cbNwN5tTuDtC3iJXnpv+h3lXzgLq6l52/sAJF9aQM8frmbFph5mfP4EVl19NRMOO4DyM79CdK+idMF16L/zZqSC5NP/Ijx7DrXnf3fo9pw3MEBm5Qoyi98iu3Qxfm83fk8XMp9Fui5px6e9d4BkJotAESsrpaSifPsmjGqCyqYmag84kMpDDiM0Ztw7NMxLLzwfIRVGLML4y366hSOFUor+h/+K07KOqrO+ih6NbtdxejuUlKz60Q8Yde55hEeP+a/aGsEIPirsTGT3ejO3Q8nul93w++7npZdeyvLly7n11luJDvuNKBQKtLS0MHHiRNrb2/nud7/LrFmzuOiiiwC46667mDdvHrfddhsAZ511FqeeeuqIG8P24EPx2e1cDvmBra80QwjTQgkj0IAqBaIY6rBF9TCI/BUy8LVV7+eXp1QwKQl/cywxalibxRhh5fGeTQltc4CE9ralbgSWVh8xpJQsenM5//rbwzz24lu8sGQt3lbId3kszOKfnUjt6IYgWrmyOYj8LWSCyWaGjfIKqEQXSIlQEjmQIbemG6GBNWEsWii62b94cKKXXnRCKK9GVDUGDgNS4nduwn39JczmUWjJdlQohtM5gEol0A45ERKtyM4O1LqlKM1EWaVIqWPtuhehXXdFJnsRXh6/rxvZvh5KSxB9XdAwmvybb+GtX4/QNMyKUkK77oqYcVBQAXzpMWTrWnxPQwvZCF0Ets0QqC8MI6hGCz2osNqDMa+S7GuLMEIWoqwCp70Hs7GG/oWLKTn+dPRIjPxz/8Lv6wYUmh3GbGgitM8nEbkUqUVvYu66F6HaarxEH05XJ/GTz2Lg6gtwCgY1l/6Kgdt+T/zUL6MVY5flQCLw0+3uxO9qR2ZSgX2ebmA0jsYcN6mYqPbe+tytwevqIPfCU7id7eRXrcB3XPyaZmShgJXrp+57PyHz0J+IHnMyelklyb/egWxdhervRkUqEJEYRk09od32wpw4jd6briUyZ38iu8/Zrn50Xv0T1j7wAFOuuZa3zjmHqpnTiIZ0yk8+ndLDNie45Ze+SeqfD+E7DtnFb1L3/Z8Rmb7ru7br53IMvLyQxOP/xF2zHK+nG+m4pAoe6Xxhu/qopGIgXyCby4P0MS2LeGU51WNGM+3yKymdNp308uW03nYLRmkp4cZGGk4/8x3tuF0d9N55a3DHYDCmOhbDqKjEqKhCC289Utisb8RqaNziNek4rPnp5WiWjWbbhJqbCTU3E24ejV1f/x9dEyMYwYeJEbK7Ga2trRx88MFYloUxzHf78ssv56CDDuKUU05h48aNRKNRjjvuOC644AL04ndcKcXVVxlcv+4AACAASURBVF895LN7/PHHj/jsbi8+jIvRue9qZF/H1lf6KpiFqBWDJIYeYthSFCN/bQiFEHbwwA4hQqGAXIXsYf/BDL8A1DAyO2gbNSxaeBh53aw7HfZp6QeE0HOKqW5O8bkbLNVHHCYxCOlBpBxlRkgue4X75r/Bub+88x1vO23fKdxy0dzALzheA4U8KpcPYpCVH4w3fA+yeQjb4Ps4nSn8vjQeJqJmTHC8NB2kQnlB0IYwNKyyKJrw0KIxRGk5oroJlenCX7MazU2jmUCsHC/t4G9YFwwUIqWIQio4LYaJNnkvvI4ehGUTmj0LLVYG0VL8157Ab1+PaBqHWvkGxrixqNopeE/dS74ng0zn0UwDe+YsrCm7oCobYdFTYIegYQIiXh2MdTo24m9Yi7Bs9LpGlA60r0X2dKKcPE7LxmCymtBQkXKEkqhUL72re4h86jOIcAQtFIZQCNmyGmfRAixLJ3rAIZhTZpJduAA/VkGoqhxRXU9h2WLKzv4mA7//Ifk1q6m69A9kHvpzMDhAoJeVo1fXoVfXYVTXIaIx/M42nDUrcFvW4PT1km/rwB1IFq9thSZA0xWaNuwhVHHcFVzsQvrk0hKvYTpayCbc1ITW14634k2iRx6HZ8XouuG3WNN2I2Z5xM/4OmZdQLZkNoX31F9wMwVcz8B581WinzqGyCFHM/DAXQjDJH7sCe+4tt4NXk83rdf+D+3/epz41F1wfIlKp4jbkvJjP0f8pDOHfrSV55F8+F6cTRvILnoVvWksDd+/HO1dbvcNxxD5/dc/kIne7fPZlTIYcCT7UZ6PLyWJTI7uviRKepy4diMAq35yOaqQR+VzTPjpleiRrZPXQSilkOkUXl8vXl8PKrd1+5jU889QccIp2GPGbX3f8nnyGzeQb2kh19JCoaNjy4THDxtKoaQEIYhMmEDpzN2ITJ48Ep4xgi2wM5HdG6z8DiW75zmhkQS1nRkfxsXoL5gHA11bXykISK2mQOjFCU9FwjtIgAF8P/DOdFyU66KcIABCucFzXC/48R1ObMXQBoZIsDB0hGmCaW4mxwK2SGV7V4hhj8FS4bCS4UcIJTSElw+qtJoO4VLO/t2d/Gn+m+9475PfP44D50wBV4JlQSgMoRjEKoJEs1ULUdk8mBZoApnL46zvw2waFTgcDMY5KwXFBDI/k8VLJJGFYCCgGTp62EQPh9Bm7Y1IJvHb1qOkj1HfiDAEcsxuaOES/BceQPkmqq8NmXNQjdNRWhSZGiC861T0SbMR1aPwHvwl0vOAELK7BbOhEVXRiFy3HJHuxSNEYc16FAZaZS2hSRPwkyn8rk58p1jBj5VBrGgd1t8DmYFgXzQNYVuoRBv4oNsm+pgJuGvXotsazkABrXYUem09ek0D+ripiKrGwHP1Jxcjcinie+5O6LNfpPDSc+QSKUJlUey9Dyb7/NOUn3M+qYfmkXvyASp+8Hv0imq8to24m9bjrF+D25fAz6Zxe7rBy2OVhjHCFkYsilVdg15aunkgV4yoVnYkqErb0WBp2kPfFyEErHoZ77WnCZ36XfSaIpGVkp7vnUvJ507Hnjyd9P23kS9tpPD4A8imCYQmTcNuGoXd0IhJDrFqIUzfj56rLqPmmlsQlk3mpefIvf4yled8I6iSbwN6/nANrz3wMBN2m0GusxupJPlkhjItS9lhR1H15Yu3kA143Z0k7r4N2Z8gt3oVIhpDlMQxR40hPGMW4QmTsOobPvDKhnRdsitXkHrlZTKLXsXZuJ5Xnn6W2RddzC4/+DFuIsGaq36BPzBAuLGesZf86AMJglCeR/tVl1N93jd3+IS7DxJKSrJr15BatIjMihUo33/X46E8j/iee1JxyKHo2zB4GcHHHzsV2bUL1Go7hrZ1SsF5BXuE7O7M+FA0u+8ap6sCouEWUE4enCwU0gFhK2SC504W3HxQYZU+72tmJ7TAGmvwoRdjf7VBmYQM0owKBZSTC6qz8j1OpRDFpLBByYI2jCBTrBAXtcUfFRTBfpglIIxgv9IJuro6mXH+b0hkt7ylO7WhnFdv/iHW2BkIzw8GEEoinBzKLUByHSqRgFweEY2B9HD6XPwsQfjB8O2qQNKglZYhyioQhoHyfdyubpxNm1DdLURHVaPP3BtdushED15PD3plBSregOpqgXApqpALzlWiA1wXWTUeZUSRA/3YTbVYnzwOVVKB/MfvkI6L7OwETUOrqEGfMB3lSuQbz6CUQvgebl8fTsFGC9votoZpKYSmgkrtYHVfGChNICJxiNeihEXuqb8jHQ8zaiOiJfjZAiJsIftSaLvMQSX78TMZ8Bw000CrrCF68pfp+9WlZN5aTHR0E6VfuwRvySLSa9ZhmhA94UzSjz5AxbkXkl/yKqlbrwLNKFp1hdCiMfRIFC0SwagfjTVxOlpFHZRWBQOy/wJ+Twe5eT/H2v1ArAOPC17L5ei55MuUfekijLoG8n//C+HjzyLz6H243Z34ySR+voDrgW+FkBtXYNXUE64oo/LCywBwWtaR+MvtlB13MkZVDVpp/D1JX2H1CpZd9TMqd9kFTWmkXn8NJ5mkoFlEk21UHngQNd/5KcKytvhc9qXnyL6yoCiZAZnP4/Un8NJpZCqFCEcwRo+n9ODDic6c9Z7Jbv8JlJS8OGsKq7r6OHHpKuzycjbcdGMw4TGXxWlro+krXyUybvx/vS2ZzdL+y59Rd9EP/mvN784IJSUDC1+i98knkYU8pbvNovKwwzFi2zfxcQQfH4yQ3Y8/RsjudkD2rYTCu2h2hRZUdAd9WwelC/8hlPTBLYCTBzcPbi547haCVDFv8OHyPmJdMMzgoetFwjxYbQ76NxRLvDNAehCOQ7gM6mciorUIIbju/BP42rX3vePtV5ywD9/9xlcg1QOpLihkARUELkQiCF0gu3oQpXEwLJSQ+EkdQsFtW6HpiEgEEYkiIjGk6yE72sB1wTDRGxrR6xqQDmTv/gO6DipWhTWqHqGDyueDYzphBvT0IPrWIPY5DnLdeI8+iERH1E3Ey/koBKbpETrxm6i2xaj+Tcg1K/GTSRA6mleACbPRd52DfOPfqL4EKt2DyKcDmYphgWmhDBuBHkz1ki4qn0FILxhseS7K83E6+/DSefSYjUAgqpuQfb0I6SI9Ao2vZaNV1qIi8cAbt6edkot/Tur6n5NduRI9HMaccwjhMc3kWjbirVtJ+Ze/Rervf6Xi3Au2i8Aqz8PduB6vezutxwSEZuyOZodQUpK541cYfhr7xAsR0Thu+yYSv76csvO+hR4vI//P+4icdN5QpVamBnDXr8ZrWY27cR3SSZF44lkqvvJtyo86FgA/lSSz4Bn8RB8yORDcWSnCqKym7PhTtuhS688uYfVrb7LfvLvILl9G66+vJt/Zjhg/DfnmS9Tuuze1P/zFVu3OlFL4PV1BFXz9Gvz+RNCHTAp3Ywtu2yb8bAasEEZ1Hcao0UFk8DZDw2poxG4eE7iKWDbCshC2TeLV13j261+hZq+9OOjhx5Cex4rvfJvo5Mlouo6fSaFHojSedc5/raP1envouv631H/n0uAO1P9RKKVILVpEz78eQ+ZyW71DoKQk3NxM/clfQPsPQkJG8NFjZyK7N4YK1O6gKTadEs7Nj5DdnRofxsWYn/dj/K530exuDYPyA6ENkzQMEuJB0jms2jq4fgvN7/BHUR4x+Nni6+81W1spFThEeO7m2/bD//Z9NpNlwXbPlv9AEfRDr46j10TQYxGIVULlOPxEJ/sdfSYLV7dt8YmwafDmlafQXF6G9AxkuBZRUoHSTYwNz6KPDm7Ri54+RFUNOFnUboejj/8E6BYKgcqkUck+VF8nsr0liPqNxdFGTUJm08h1yxCFboyxU/Hfeh7Z1oLnaWi2gURHOi6+FoWqBlTLYkCHSBmhmRPR16/AS/totaPwUgWIVyK6Wwif+R3EhpdR0TBy/t+R4VqIViB616GUhopWo9XUIEKlqPa1ga2YHQGnsFn+YtpBZTVWilbXjFbTAOk+VPsaCo/cgZdx8PMuRjSE3jwWr70LvSyKuf9ncVYuQbaug/4+lPQRCGQkDm6B2AVXkP7jFThtrZhNzWT681QedQxeIU/6uWco/8LZZF/4NxXnXfQOIqM8LyCYG9dTWL0c2Z8oykS0YrTwdg4CfR+/rwt72kyiBx2OEILcgqeRL/+d8DFnoI3dhdwrL5B59D5KT/sKejRK7tF7McZPwdpld7TyzfZdqpAn+ZtLCH/mi7T99BLU5D1p+NJ52PUN77r53luvo+yE09Bjm+8EZF6czxvXXce4aROpOucbaLEYLZf/iMRTjxP51Kfpf/Qh6mdNo/7Sq7BGb127+l5QUlJYu4rM/Kdw121fupCSEi+RwBtIAAIRiQaTxkaNRlM+a26+jeUtGzl43p9p+tRh9Dz5BN5AEquinN6nnqLm2GPp/MtdNJ37ZSITJ21334ej0LKOxF/vpvbC7/1/Hx2cXrKE9r/chVVTQ+PpZ2KUlLz/h0aw02CE7H78MUJ2twOqvy2QI7xzTfAfuiw6I0gfpBdUF30nqMo6OfDyw0imF7x/cDnorjBIPLc4LWrY8+JyaOLbMMKs65snqmnGf1VZ/qggdQM/reGvXB545/o59Ko45ux9eGP+4+x1wfXIt8k1DttrNqefcgrLOnpYtnIlS5YsYfXq1cRtnctOPJCvfHoWclMnoiyOEgZaVTyokA6R+uLkQa1Y8bXDkC/gd3Qh+5MB+S2rA8fBKCtFxiqRCx9DFhSa4aPGTseorodkJ6KmEeU6qO42sskQdK8mXGbjJkHoAl9aaA1jkWvexNxtNtbk8ahsHrXy9YDE1k5DrnwFYQi0umb8rAuRiqB/QkNU16PXj0arbQquGcNEZVL4G9cg29YHMhqlYNGjOD1p3FQWqySMCIdRmo2IV4D0kFoIs3kMxox98Hq6cF55BrVmKZTXogp5Yl+7jNQfr8DtaCc8ew7JVWsp/eRhaNIjt6kVo64Rv6sdvXQwcnjwutSCW/i6gczlkbls0Hddxxw1BrOuYbvGUjKfJ/PsE0T22pfCGy8TO+xY7Cm74La34tz1C8Lf/BWaYZJ84E689asoOe40jOZxyI5NuEteQ/b3gqZhjJuCOWUmuQVPIrpWk+tMY4yewMC6VoRpUnvKaZhbqcQ6mzaQXfgcZcd9Yeg1pRTrv/dNWjyLSZVhqk45G2v0WPqeeIx137mQ+GFH0Pn4k9Q0VdHwo58R3eMT2/EN+OAgM2nyq5aRe+VF8iuW4fX1EDrwcBZc8n1CjY0c+uyLGLbN+t9di1VRSfn++7H+V79k9IUX0/O3BwFF6ew9MKuqsKqrMcrKt1vXm33zdTIvL6D67K/umJ38mCG3oYW2efPQLIvGM87Eqqn5qLs0gm3AzkR2b4q4O5TsfilrjpDdnRkfCtl1UgFR/QihBm3HvHwgcXCyRV1wpih1CLTBeIN+vlvBYHXYCoERAiu6eYKQGd48me5DhwJPwqZF4BdQ4/YFLYz/xlM4LzyJtc8n+Pa187j24Ze2q9Unf/ZFDpgxBrVuPVrzxKCaaZjD7NsUanByny+Lbg46GIFzhtJ0vO5eqB4FBRfTlKj/x955x8lV1vv//ZwyfXZne0l2s+khjRACgdC7IAg2QFBBUK/itYGKivrTq9eCApYrIKAgNsoF5SIoRQFDSEiB9LrJZpPdbJ3dnT5zyvP8/jizJWQjCRIS7t3P63VeZ3bOzJlnzjw78znf8/l+Psdfgnz6XmS8zzvePh9EytFjMURJAHHKVagXfoNdMonss48RnVSPG2lAbl+Lio1DGT50CshclsA5Z6O1b0bNPh+1/HEorUd2bkdZoJLd6DMWoITh+f5mk94JUtGrGc0Hus+7fBopQxs3Fa1xOtZDP8Te1e7JMhwXI+BHVNejfFECZ78burbjdu3B7k8iMylPkhCKoja9AhX1KDtP+Jovkv7F97H74gQXLCLT2oYxcRqBoIGYOpfc8qWI13TxC01Di5bga5yIf9pMjMoqlOPgduzCaW3Gjfcc3JTQNPwnnUP/735J6MTTkD0d2Lt2UvLeK1HbXyH3ygqin/gaAAN33wpSEnnn+zEbmoZnlevi7NhMYenf8J9xEZn7byVy3dfp/fpnqb7lPqzODnqf/DNuKll8gveVaMTKKDl+IfkXn6Xq0zfuNazsqmUkFj/H+s2tTKkro/Y9lxKccwxOYoD1F51HoL6OZH8KfybOhOtvJPaeQ+8j+XrY+fEr8NfU0LF0Jes3bGLmVdew4Ps3A17YxMCyZTRdfwMtP/geVe+8kGDjBLLbt2H39npLf/9wz8L+TqSFoOlLX96LFCdfeBanq5PYJZeivUbL/H8VVm8v7ffdi5vNMu7DHybYNPFwD2kM/wRjZPftjzGyexCQrS9BbmCULSNlAKNsE3rRy9YYrsBqg1Zhxl7rt+Jyn1ISnIJXbbayns7VzhWX1w91OIQjg0LKI9uVkzyJRSrukb+tS8nvTFAIFZj75V+zJ5484L2eNKWe53/wIeiKg9+P9oH/RAt6FUmlJMoqeCcM+TRI17Ms696Fu3sbJHrBTiNkBtcSUD8VNdCFaUjE+f8O659DbVqGTCWRmoF0dMinEIYf45z3ofW0IMOlJJ95juC4KozjLsB++g8oLYTrSvzjy3FyLkYshHnUsWi1k1CJTog1Iv92LypWh+pt86qjwSAiGAXD71muhWOQ7kWl4yBMlDBRyX5UOombSiF7u5GWi5MpoId86OMaEaEyzFMvxmneCHYBzUmjRSOISXNJP3QvWkk5audGRGU9ynHwve/j5O69BdeyCMxZgBXvw7IhMrGB6NWf3YvUKKWQ/b24Hbtxdu1AJr3/FaHr6PWNaHWNKIZlDEq6qFwGshlUNo1MJ1DJAWRyAJVJevrjXBah6ZRc/x3SzzzhNQRd+F5Sj/4OAkGCTjcFvZKSS69GZjMM3PdfmLX1yKyXdKgFw5gTp2JOnIoWLSX74F0Yc47DfvmvaNNPIPfy81R88T/3nYlK4Qz00/azH1M2ZyaBmXMJTJ+512Pym9eTePIxtvamKbWzTHrXxUROORPlumy66gM4ba3os+aRev4ZJl7zEao/99XD6inr9PWx+7oP4p89j7X33o9bWsrC+x+kcvZsADLNzbT+9CdM+upN9PzPYyjXpfyMM/BVVWOWHVhVN71+HT1P/JmJN35l7/uXvUh29SrPcWYQmoZZXYMWOXyX9IVh4J84Bf/EyYfls3HSafb85tcU2tupvewDROfs3495DIcPRxLZvSfiHFKy+9G0MUZ2j2S8JdZjL/8ekvuxHtMMMAZDGobJrfcDUUxTU6MsslihG9x+oCj69iJ0hu3JYFh3O/hncSyDpNoYlDkMhkkcYVKHSB0ESqF1qddwZnoRuKq3E5XsJrelmz939nLFbfs2q/0z/PWmyzjrpJmoHTsRdY2Iqoai7nlkU6E+esXKyuNuXo2ggOtoiKbjkLvXYtbGELWzvEbB1g2oQBSV7AFNQ8V7cAYsjOnT0cqqUeTIvroVoZuE3n0NzouPY+3ciUym8E+tRTRMwdmwEWPmAswZR6EFIlA3HfniHxCTjvO8hKNlCDGSXMqhv1VuAPasR1lZiNZS+O1tiEIaK55Dui7KcTFKI4jKBoQ/hKgahz7reLRICe6OTchtq9AaJlLYuMHTA3fsRFQ3gnIxzr+c/P0/QfmDmJNmIHMZUru7iU6bjK9u3HBDlxBe2lnteIjEsPa0k137Ck77LhjoRtgFdNNAjJyfhuk1yxkGGD5P1xuMQDDkWZPpOtbSv0M2hV7fgMzlKfT145u3kODMOTgrnsPX2IhWP5nIeZdgt+0k/fhDhE49F9+0WSi7gN3SjN2yFbt1B/6pMzDqx5N7/DdEPvE14j+4idi/fQnfxCmjzhurs5PO391PKOKj8t8+t+/2tl0M/OFX9FQ00LfkH8y56J3ELrkUpRQ7vvUNMs8/TfiEU2h/+EEazz2d6Lz5mPUNmBMm4WuahF4SO6h5/K+i886f4GxcTXxLC2vXbqDp1FM46fcPoxWJnpNOs+1b/4+6Sy/DCIVJb9yA1dON3T+wtxPNfk7KfZWVRI46iszWLYz/2L/907Eo1/U8gdPpN+39HSyUbVHY0UyhZbvXFAz46scTPGo2ennFqM/RI1H0aMmbOg5pWXQ++ADpjRuouvAiYotO+j+vcz6SMEZ23/4YI7sHAXf5byC5n8uwcjgdDSmH14OHd9CHd2Sz2qAHr256XrCmr+iaYBZ//Ivk2TCHqyqDWl0JHkketJ/Sh4nbyCY3KT3dsFvUBw/dtkdEFx9BcIo2akoHYXgVaOmABqq7C+acSeaB+7n8yZX8ZdWBN+8smljD87d8BNHTB6Ey8EdHnDCM1O6O8mSpUMkelBSgOUgXROPxyC3LMJsq0Waeh2pZC4kuEAKlB1DKgp4urN4sekkQfcIURNSHtbGZfDyPXlGH6RcoO4u1di2+cZX4zr8Q2d6LvWs3WsiP/6z3otVMQK14DMxBP0/PacLt7cXp6kKl0xApg7I6L0ACBfkkWssK0AV2Rx9SSqTtIIRG5OLLMU57H/amVbgbi5pW10WFYxDfjbnoXOytm7CbNyMSvYi6CSjbxn/5p8j+7OuIihq0yjqElSenR7wmOcdB5bzqrMokEfkMui4wwkH0WDl6RRUiVoUSGm7BOkDdp/KkOI5N4PR34K54Aae3i8g1N2CteZnkI79B8/lx/WF8uoVV0IicfQHh085DZjMUNq7G3roR5XrhIP7Z8zEaJ5G4/3bMoA//KeeS+9N9hK78LPHvfZmaW+/b70h23XIzfmFTdd0No4ZCOP1x4nf9FHn8qay943aOPv1kaj7hVb1333k7yacex1dRRf/WbbiphOfhqhSBSJBQfS3R6TPwT2jC1zQF/4xZmA1Nb4rn7ahH1XVp+eSH8YWCbHv6OdKGSf2VV3PsjV8efoxStP7Xz9CDQcoWnYQeDg8tWiDwT0lY58MPYZaVDUlCqt/93kPyPg4VlFLYe9rJbVqHmxjtKh64yQQynQJA+AMEph9FcOYczKqaf/31paTnz4+TWLkCzTeKc4Pywos0v5/Q5MmEp00nNGXK64aCjOFfwxFFdqPuoSW7KX2M7B7JeEvI7rZlMDCadZIq/gD8kzNxJT3SJhhBRotuCcoddkhQRQI7GGM72NA2sqqi6a9ZigTY9IEv4Okvdc3TpQ6RaN9rfqQEQje98AXd710a132eBvRwVhR8Ye/8oH8b9LUAGvhKYcPTKD0IykEJg55l2/js04t5YcNuNMNk1sTxzJzUwMymWgYGEnzlrkf32fUTn7mIc885Brr7PcJr+ooVbn34k/MFoWIcRMqGD4ProLp34m55xTu2fhNpO6j6ecjNi9GrGxGhGFpmD2LKAsgPoFrWo6onQM7Gae+AQgqjrhpx9HGwdTNUNWHlfTirF2Nn8sjdO/CVhdArKzGPOwvh92GvXwWl4/DNPwkZ78btaAMUGAZG4xSMSdO9hrPuHagdr0AwiphxMiIQIfODf0MIB7erx5s6roubt/FNn0boA59Dqx+uZKpcGmfjKgp/+yNC2hgnno3MFsg9/Se0QgZR24gqFAhd+wVSP/gSxqSpSM2PZmWHtM0EQwjThwhFUcEwqmB5chlA+PwYjZMwKqvQlAvZFCqbRGWSOIUCZNPI/jgymUBmspDLeOletoV0XERpOfrUuWhuAVyHkuu+hnJd4nfcillfT/7pR/GNb6DQ0Ung1HcQu+zqvT53mU5SWPcK+VeXYU6ZAZk0RnkF1qrnCF12HanHH8bt6aLkI5/CN35f7aTd30fbD79LxamnUvKOd406bWUuS+/Pf0jg3Hfx0g9+yJQJtUz62rfRQmE6H3yAxN+fQuQymJVV6GXliGAY17bJ7thOZvs2nP4BlGMjdA0tUoIRK/McLHz+g/p/FIC/oZGKs86m/NTTMUfp+k+8+A+SD91L/9oNrG9uZe5FF5KsqGPhN76BPkJTm3j1FbLbmnGzGdxMcckXGyD3MyYnlUQ3DJpu+CI9f3qU0NRplJ12+gGP/+0GmcuR37qR3MZ1OL09o/c7KC+h0T95KiWnnzOiqfONw83nyW3fTmbbVrLNzbjZrPf9/n+0GhxbtIiKM886ZPsfI7tvf4yR3YOA/fD3kb0do28cshHTvUvLI6u3uo7wBSEYRgQiXrd/IIjwh7wmMZ/fe45T9NEdXCs1IvRhcD3C9UF5rg8gPS0kcth3V6nhsIRB94fBH6khjfDIMXq+q3tVkd9yKJSdQYSqENE6lNBRsgCZTuje4r2XeA9i0jHIHRvJbd5JYFodWjCKkhI3lUUmC9jZLItueZgNXf177f2Epmr+8cNr0MyI5+VbDPrwKtxehVw5LuRHaJcN04twDpagpi5CLn7EKwaXluPmcijlQ1NpGDcb+ruRPV0Iv4YKxNBDBhgCUT4Vp3kNsrsLozSAdv5lsGYloqwWJh+Ps/YlnEyKzDN/QVl5zHAQoyyGVlqGXluNmLgAvbIOQhFUzx7knp24nbuQ8R6UlAjTNySfEbkBlFKoQBAz2wV2Hiue8zyBHYnjQHjRyQifiXnOB9Eq6oaOj7NxJblH7kFES/AdNQdVO4X03T9ClxaipgHpuIQ+9O+kb70Jc858HFdHK6scmi9aKOJFBpdXovlN6O/C3tOK27oDp20nKp3a98qGAGH6EZEoWqwcrawKrboOo64BffxE8AVJfuPjKNfBOPu9FBY/g1lRTuzGm1FKEb/7p4SOW0Tmj7+CaBXs2go+P8akGfjnL8J/1Bz0WLk3u1yHgbtvQxgmRtBP8NRzSP/hDkq/dAv5tatI/fG3yEQ/5rgJlHzgoxj1DUPHpu32n6EPdFH31e/sf/Y6Dr133kZo0Wms/O0fiGX6mPnN72LWjaP3qb/S98xTBKorva8KpXBTCex4HDedmOig/AAAIABJREFUwaiqwqwdh6+6GmdnM3br9uEUr4MkMK5mYBUcssmMFzyj6fjGjWPKV75GsHECAK3/7yvovW3sXLycXbkCJ1z+flp70xz/jW8Qrqt7nVfYP5KvvkrPM0/jxHuZ9v2b2Xnz96m64J1E5sx9w/v834J881ZSzz+Dm0rib5pE9IxzvZOaMRzxOJLI7i9LJDWHSF7e5cK1SW2M7B7JeCsmo/XEL1E9ba/zKAmIEVpYhSpqcoV0hi7N4haJqutVcJWSe+twNc37odOKnrq6iTD9EAgjghEIlUC4FBEtg1AJAglWoejSUEDZ+WITWr7opTs4HAW4RdlFUcagFS/fy+L9h7M4EI0hIiXg9yHKJiL0oGeVte4JhJNGaUEwNbAtZPkM8s8+hgiGEEKhl0bQy6NoJSU8ungjl/7Hvfvs/rFrzubCD58HZmjItk0Nxia/pjKilPKIb7wHejqgog5x0geQT/zcu7+uAYGN7BtAukEoHwd7NqOVxiAYxe3twBzfCIYGtbNQm5dibduOURpATJ0PfR1oNQ2I2WfirH4RfXw1sqeL7N/+gpV0EXYOszSMHo0AApUrIB0bKT1SrlQxVtof8TyDneKVA6Wgr4NwYwWaX6fQOQCaQGgCayCHilYhSsogHfdOziIxRKQU/4lnYwZ18n+8F9EwDX9dFdqCcxn49mcxhERUjUf5AwTOv4zsXd/Df8qZGOVeJKxrWbgdbbgdbaiBPk/3Wwwy0avrMeedgP+U89HfYMpU6u6bsZY/T/STN5F+6k84HW3EPv8fmE1T6L/vToJVISivp+9vz+OPRRH5FLKrHZVNg9ARsQq0+glEzrqAwtoVyEwaf2UVcs8WjJPehX/2/KHXyq9+mdSffo9MJggcfwqll1+Lm06z87Mfp+Fb38M3fsJ+x6mUYuCB+9Bi5Wxv3kX+5X9wzBe/TPDoY4vVuGay27aS3b4dmc97z7EtzFAAXdo4fXGsnh6MikqP/NbUHjTZFdLB2bMLu6XZawhTCik0ulo6WPj3fyCEIN+ynd4Hf0Pqr/9DsmYCW19ewfx3nEO/EWLSFVdSv2jRG/qcAHbdeQfC0NH9Aequ/CDbv3ET4z/+SQKNjW94n//bUGhpJvn3Z3ATA/gaJ1By1jswysoP97DGsB+Mkd23P8bI7kFA7lyFSo2m2VXguGBbKNvaa41te7f3STob9MkVe1/6KqaciRHm+wKJkg7SscBxEI4FljWUpKYcZ4QhxMgQixG3NS+BSww2qxUb1pSme0Nx5XDF+HBBKVRfJ8rJowV9iJoatCnz0I46F7nlRWhdAkYY3Jwnu3B0RMD09LeRSs+CywxArB4ZKGHejBms79i7unt8UzXP3XQtRlXNvg2CSPSKMsRrU46Ui+xvg45OKKtFnPoh3CdvR6STiAnjQQ958cDROpRjQyKBazngSERNLbqTQkxaAIYf2bqewtoNGDEf1M1E69vlfRaVTcjkAPrkKWjVVaiNS7DtKLm163HaWxE+n5eIFQpiRMLopSUYpaUgC4h0HOFYXrJaMAaaRvwvzxEYV0agtgq7vQMHA82x0QwNpRloAb/n5lBajRaLYkyeTW75EtxwFcHxtVgv/BkxaS7+ygjaqe9l4MvXYOgKVVaDPm4SvpnHkPnDnYhgMQ5WgFZehTHpKHzzT0SfNgf9dbrblXS9QI9M0ktwS/Wj+nuRA32o1IAnZchn8Z1yAb5jFlHYtpH0zV/EN38RlJRhbdmAOXM+kUuuIPHI7wm4nQSuuJH+Pz6I1b4bLRAgfNwizJoa7DUvk1/yDK7UCCxYhLIKaE6B4KLTSD/8K0pvvHXUKxpd13+E6lt+hRCC9jt+hp7up/aL33jdqZz+x98oNG9moKaJnffdw7Ef/iCxi0bXr0rHIb12DQNLXsRJDKAcZ4j8eiefBxPCIbH749h9/ejREoyqak864TPZc/fPiV397zR94hMAtP/4h5jKov2+eyn7+Kd5+ZZbqJk5g7Kjj0GfNI3Z11574K87AkopNn/hBsxYKfVXXElgfAMt3/02mj+AFgwQaJxAoKGRQGMjvuqaw3gl6chAobWF5N+fwu3vxxw3ntKzz8eoqHz9J47hLcORRHZ/FeOQkt1rBhgju0cy3hKyu+FxVK5vP1tFkXB6h1NohqeBNUxvbfqK66I+tmgfBYDyaotKOlDIQyGHKuS8Cm0hB4Xi2sqDZXkVR4rLoFRBqeLLy+EK7WDAhTtiXXzs8DKiiY4jQPNl+hFzzkJEY8g1f4d4G0JTUDMBPWKCkwGlIyprUR2tiNM+jiit9areI6BySR7+xse47EcP7fMS//Ppizh71lQQBkoUAzmE6aWpJbyuc62sEmPG0eiTp3shCc1Lke3roS8OVY2Iee/E/dt9iPQAxGoQQQ0VT0DleBjohVQvUi/FyeXwHX8aWttaxFkfR7WsQO7chFMw0ISDNmEOavsK77hPOxG59VVUshtj2jTExJkI24fqbPEa1AQIfxhRXuuR7rLqofctpYvavhLVvAxch+w/lpLd00/pUbWQz1FIWOiGhhbwgS+IzBdQtgO68JquhI4x72RkPk++P4dPy6N6OhAN0/FVRdEWnE/im9ehCRci5fiOO43w+1+fDMl0AnvzGqwVi73gi0x6aH4qJT0ZRrHphhFhIYPhgwjQTB9GwwRCH/kiVNaR/PLVKKuAqG0gcMr5FLasxz/7GJyNLyMq6om864Pea+dypF9+keyaV0BKAjNmYv/lAfyLzsLt7wPpYlZUYJCmkNUIHHMCvjkL9op87bvlG4TOvZjAnGORhQI7rnofk3/7x1FjYV+LQvMWEo89hHbGBaz83neZPX8mkRmz8DVNxj9x8pC8Yp9jNoL8uqnUwf1PKoWS0pN9+Ex0Q6C5DlZXF5lXV5KROnP+8AjBceNwEgN03HU7Ppmj69FH8S88ldaVK0lkssx514V0Zx2qjz2WUHU1waqqoUU/gPhfq6eHHTf/AOXYTP/hLWjF4+VmMuTbdpPfvYv8rl1YXQcZH/1mo3i8hKYRmjqVyNHzCE2ectgs4qy2XST/9lecgX6vMPFaFL/7hT+Av2kS/omT8TU2ofn3bZwcw5uHMbL79scY2T0IKCfvRe+OunGQOLqehY1rDaemOXnPnmpw7RYrvYOJaeKffAQjPXpHrkfaiRVJsxCaR6h1//Ba86F0HwgNYVvDfrpWfoS3bs77gRyUNhwuKDyP2ZYNSMtFBUoRus8jQt070Mpr0CqCoAch7IdMGoJ1aAsuRnXvhI4tqFzCO+5KIjWdY6/5Nus6X1PdndHEkts+hxjUPCtn2AoOUK7ETaWR8QQylQNAryzFWHAKtK2F/gEYPwMxYR5qyYNQMw5sBxJx3OgkQKIlO8DO4iazuK4P/3EnQHcL2rtuQK19AtW6A1cqZFbif8dlyOZXUB0tSFcgJs/HXf13SPeiz16EMe8sT7oy6BYxeCVAaBCKeE1iIw9jZoDCXTeQXLMbf5kPf2WYQkcKEfChlElg/nxkoheZSnrpbbksynERhoZ+8iVosoCtRXGXPoFWVY9WOwF/WRDVdCzpn38L4RZQgSjBCy7FnDEXGe9Gxrtw+3qRPR3IgTgy3o3K57xKN6BpOgQDaIbpOVZIiRh0ItF1j1wManl1fajxTSmFbGvByWUxysoxJ0zEf/6V5Bf/lcLiZxCVtZR+6QdYzVvIr34ZI7MHu2IaRnWt5xLhup5fr+1QaNuFk0hgtq4j8pHP4rRuR2XThE84CVHox42Mx1q3EpTCN2s+vtnHYG1aS/rJR6n4kqfVbf/hd/DX1lH5oQOrejr9ceJ3/4zQO9/D6gceRmXSOAP9+JRDSWmUWG0NwZIovsYmQseegFk37g3967wWSinyrTtJrVlDZvMm3GwG65WXUb17sI4+hXl33IEQgu7f3kfkuIXYm9bQdffPyWYc9DlzWf34E8w9/1zKZxyFbVnY+QKFXI5COusZweyHDNpKcNJtP0YIQfz550isWImmC5qu/8Kb8r4OFZTrkt3eTHrNarLNzUMEePTHOgTGN1B6womEpk0/LJVpmctRaN1BoWU71q6dnlf4kVCsOEwIzz+OyKJTD9n+jySye28Z1OiH5nPuchUf6R8ju0c03poEtbRHVEfDkHet6WkE34QvHaWU11hm57xGKivvre2cp8kd/Hso1W2w4lu8NC+Kt3VjSB7hkYuRy3CssNCL7gSHU7QbrEQEy3HbN8HKP0EgCGV1OGuXoCzQDIleXwVKIJqmoLZtgkh10YnCjzANj7wKgdq9g0eefpnL7npqn5f58x1f4/wzThgO9xj03B0FqpCj8NxfkZ2dmHPnopMqEt4piELGC56YMAfCAdi6CbHwHFR7N7J5OcLvx+nowM1J/DOmQLAEbcosVH8vKpND9e/B7snhO/kctHQPqmoCav0/cGUAlemDgTZEw1yPBOaLFX/pDjfUWQUAhKYhKsejNUxFq6zDfen3ZF5aizWQoGRWPXZnEukLYAiFKqtFhCKYU2dCTwsym0H29+P2dKNFo4iZp2D4BWr6QnJ3fQutfiq+2fMxtDyOWUH+8fuhkEP6w57dmfIqiaJ4hUIIgQj4PcIg5bCUxhdAhCKepZ4ZQATDiEDIk434Agh/AGUYYDuogje/VT6HGoij2puR+TzSCOBvGIdR14Db30th3Rq0+kZiX7kVhSB113eRUqCPm4xWVo5eVoleUYmIloJtE7/7pxT2tOPP9hI452JkMoEZMAkdtwARjKJNOQblOlgbVmOtX4VWVUvmqceo/tGvAK/quv2Ki5n60BMHPKWVbRG/66cEZh9NcN5xaCWlZLu7iW/aRHzTJtLt7biJAbTkAFW1FVSMr8c/ccqbTn43Xfth5I7NiJISopd+hMYPfhBp2+z+9tep+cjHIJ2g67bv0rt6A+Xvv4LV9/8aWw2eYBW/E4TA8Pkw95OEFvEZVL7vcuZ89vMA7Lj5B+C6lJ95JrGFJ7wp7+VIQH73bhLLXiKzZQsARmkJ0Xnz92sBFpo2HWMUZ4wxvD0wRnbf/hgjuwcBldoDTnaUDXgES3r+tWqoOvo6yWqMkDuMJMuaufdt3QTxxtLVlFJeo1ohO5yYZmVRg3/buSHi5EHuZ7xvBRRoNsQqEbEJqGgjbF+JatuAyvUhO1tRWQchJHpjI1RWIPwBwA+BKIQqvLV0PfuvTYuRlsOC637C2s69PTPnTmnk05eeQ8hnEPT7CAX8hPwG5SURqmJRyqJhdL1YsZEOBAzcgQT26vXITB7/tFqEZUNpDJFOQMMsRDCI2r4Bps1HHHMJ8nffRMTKUX3t2AMWMpHC11SNNnk+wqfB9NNRaz0Sbackxoxj0PI9iJoJkB4Au4Db1Yzs6QQziIhEEbEYejgEhu59ZGWNiCmLwJWolvW4LRtQyT5UVwtOdw9Wfxrh0/EFwMlJjLAfSqogWoZKpVC2hVbXgFFVjbPy77iJNHrTZIg1oEcC6HMWkr7rB2gVdQTOuBAt1UG+L4ez/DnvKoU/gChWb4ca/nQDLRBGq6nHnHM8xvQ5CNOHu3sHbtceb3zWYCOl5TmP2AXvpE1oCH8A4fd7JFgInLbdGCecS/6ph7zquQt6VR1GQMc46QKyv/k5WtM0YjfdCoDz+B2oiceiND9ufxw50IdMJXETfYQveB/Jp/5M6pk/E6qM4Zt5DMqxCB41GzOs0GcuQlQME8zUH+4iv2kdZZ/+mqfzBnZ/9fOETz2b8ne888BntlLkVi6lsG0LbnJ4LuqlMXyTphKYdTS27dC2eDGdK1fi9PehJfsJBQ7OClAIQUllBaU1VZjlFfinz8Q/bSZ6tIT0xg3svOmLyJ1bEadfyJQvfolgvZeUt+fnPyY8ey6Ro+fR86P/oOfFlzBmzKHyne/CjMXQTO+kWDku1kA/VjGh7rVY9p1vUxkJMuee31IxZw7Sttl0w/X4YqVo/gDRuXOJLTwBX3X1Ab+ntwPsgQFSa15FFQr7bFNSkV6/DjedIjrvGCrOfceYL+7bDEcU2a0Qh5bsxtUY2T2S8ZZodlMdYI3+JT8kNziIqu6Qx64ccTldvmZRxaAKNdg49pomt9feJwYlDsUqreYbruJq5l4JXEcilBGE1nWQ7oCSCMTGQbQJ9cxdKKsft+CH3l0Inw99ykREXRMERnT4FyUbKBc1sAfaO/nvh/7K5b95/qDGoWmC8pIwVbESKkojTK+v4PqPXsKMqihuIkN++SqMqB8tZCBiJQipo8onoOwsanszzDoZc9YC5JN3ok2ZAak01o4WZDqDrzaGGN+ENm4mYs7ZyLWPI9e/Co3H4PYNQCGDHtLQp81HtW0GYYGdR6UTqHzOuzzvOMi8BYWc5+YhFcoXhWAE4QuietrRTEl+1wB2sp9wQwyrJ0tg3izQfciMhUolPE25P4Qb7/VIdKoXJ5XFd/qFyIGMp4iZdBTJB3+NGYsReN+1sHsD+Y4+1J5diEAQfdIMzBlHo9dP8NLj+ntx21txdm1DJfshk0D4TLTyCrTSUrRIDBEtRQSjiFAJhKKIUBQCob0kGV5ccAbl2uR//zOU0NFnLqDwx3tRBQvblgRqK/F/4puk/+Pf0eoaKfvW7V7j27oXUH2daPPOQpTVFPfnkPjljyn92PWknn2Svp9+h8CUqfgnz0CvrEbkkgTKfBjnX4MIeI13maf+iN2+GxGOUHrZNQDYPV3s/PwnmHLfwwek3f1ncAf6KOxoJr9hNTKd9uJrj5pDcN4CbNshuXv3Qe1PuS59W7bQt2ULbjqFE+8lgKSsvITx77mUzsf+B7ltHTKVwJ59InNuvXXo+yr+2KNYnR3UXH0tfb+4jeTyl8ln8rhZT8qjNA2K+XdqPyfESsG6deuZPKmRhc++iOH3k925k7Z77mbCZz9HftcuEsuXYXV7jb7++noC496c6vUbgTBNSo87HjN26JPslFKkVr9K/OmnkPkcJQuOp+Lsc9Be2xA7hiMOY2T37Y8xsnsQkCt/uX+yCww1jA111xQT0tBGBEkYw568g9pawzdc3R0kqYONbcXL7AfCn71PshhEIe3XrB3v9qiJacOXKL0ktoM6LG8yhPeeA+XgAHs2QQBU3oHW9ahACNnZiUolEYEgxvHHQVnjsI2a6yCzOdSu7YjaGkTPbtxUloVfvJvV7ftrLjwwhAM+/vtHn+Hc+dPBVtg7tuDu3oObziNMAz1WigrFEMpBKylBBsfji7i4Ha3oIQFzL6Lw2D3gC2IGXNB1tEWXIWprUK6DWvw42HnESe/HaWnG3foKWiiAefQilG3jduxGduzypAxCoJWUogWDYJooHMTAHrByqEAp9qZNGOOqyO/oRagCTiaHYeiI2ccROeFk5LrnoGoi0ojibn4F2deDtF1vnlkZ3KyF/8M34LZsQ0t0oo9vJPnkE/hKIwQ/9BnkhmVYvUn0impv3hWyqFQ/ws4jQiG0WAy9rhG9fhKiZgIiEEJlksjeDs99IZPy1vkRV0qK5vtYBU+e4booBVoohBargFAJ+b/9Cd9xp2G9shS3fSfWQILg3PloMxdgPfsYIhCm7Pu/9HZnF5CvPgv5DNqx5yLCMazmTVjNm4i84z1k16yi92ufQlTWUnHdF7C3bMBevxLDD5Ebf4wRCmPvbqGwagm5Nauo+n+3DQ214z++TDpVYMKXvoqvqupfmlcjoRyb/MZ15Fav8FK6RsiMDmwHyiPMU6bjnz4LvbqWdFsbe15+md2/upPTfvMAWz56FezaRsk1n0KUVdF45ZVDT8+sX0v8kQcZd/2Xyb/6MoXmLZ6e3yp4kpJ8DpnLodzRtf3pjRsZkCabV73KzFNO5NgH/gRAav16ep9+CieRACA0eQqxE05ACwWxuvYTwf4WQBbyJJYtxUkkMUpLKD/jLKJHH33IdbhKSpIrV9D37DNIxyZ24iLKTj8T7QCa/8bw1uNIIrv3VerUGIeI7DqKq3vdMbJ7JONwT8YhhwTpolzbi70tZMFOFzW3Wa9Bzc57ut9Bre1g9XawWW1wP3utYYhFi9dYiomi1nSwsqz7i1XckQ1sxeX1fjQHm+wOGxQUkiAtiNaDEl6124yg9qyBXbtBZaFsMu6WFai8hYhEoKoR1dmOynsnIsL0e/KBzl0Yx86DQoFH7n2cyx5Y/C+P0NB1fvm1D/PBC09HDQxATwv4Ix4BzeYQCy5A9rThvrIMOe5ohJKYoh8ViqLhwLzzKPzuJ1A3BZ+IQ8Mc8Jei+nagzTkPymtRz/wKXBdx8vuRuQLO839ElMQwpsxGmzYPEa3wrgYkesAuoFwX+tpRPbuQ+RT07cLash2tpBRnIIPK5XCFhmbnUMIkevmHvUvSHc3Q04aobkJOWYj14O2oUCkk4iAt3IIi/NnvY7dsRW1diRYrJfmPZQQqo4Su+BRyy3JExKuIidIKtPrJiOpGT8rQ24Hc04LsakO5NiqTRhby4EjUYNqf4UMhELblkSnbBiG8aVp0F/Ea1QxEMIRZUYbKpnB2bEHZBYiUY29Yhd2fxDdxMvqE6djbN0GyH98p5xM8+13olTWoXBr5ytOg6WgnXETqwXsJnfVOjKpa0i88Q+Kn38IxQkh/EKO+EVL9OC1b0Woa0GvqiUyZiNW8hcqv/wgt7Oku7a4O4vf8jEwiR/XlVxKdv+BfnltvFmShgLV9C/nNG3C6vBAcYRjs2tVFKGASrqwm+49nKWzdiHb+pUz65CcJjgiSsPvi7PnxD6n+8LUEp0w9qNdO/+0v7PrWV4lXNdD9yiqO+fRnmHT9jXs9RilFbscO+pcuJdu8zfseO1xwXdA0YieeSHTuXBJLl5Jas8Y7adgP8fTX1lLz3vdhlr05gRDKdRl4aQn9LzwPKGKnnErZyaceNkeIMeyLw80vRo5hjOy+MYyR3YOA7NsKhcToGwfjgl+HUHpFX32YpO5FWEeu975PCYoBFINEueBpJu28R6zt3GsSweQIAi2HLcYG09IGtcCDlWXDD74o+EKDozwsEKEKlD8CPRsg2QZGwKvyxltQWQfaNkPTTFT7DuRAvyfTqBiPNnkWIlgKfXs84tffhdvXg6isQ9ctrLTDVT97hIdXbX9Txvn9T17MF659D2xfC5YDoTCqvBHR1gyTp0JoMnLds9iqCi25Gz0cRisJwPhZKJ+O9eRjEArhC0q0M6+G8ZNQG55H9aXQJh+DKilHvfB7r6rfNMfTtBZyKMeCfNrTW6MQvhBEyyFSBr6gZ+MlXZy/3IWTU+jBAPmeLEZYkGvtxIxG0GvqoKwe32nvwmiaDCseQ7WswdX82Nt2QHUjqmMHQrpIgoS/eAvO7hbc5c8gNIfkqg2EGmoJfuBTCKEhe9qR/T0eQbUKqHQS5Uqkq3AzWeyOds9yTGieDneE5R5KIfTBEzcQg7HaxSY85dgoTM+yTzfQTAP/nGMRA3twd2xEP/YM7KcexlY6ZkUFom4iRn0jheWLUYm45+xgmohwCb6pkwkuOgsxYQ6J+2+n9KOf91wDfn4zzuY13jGVChdPBqOZBlqsEjeXA1+AkgveTfj0dwzNAZnN0HvHrRRshdk4idorPvimzK1DAZnP03vnrSx/7K+c9Zvfse3Tn8SMtxE+4x3EuxNMu/FGwk1NQ49XjkPXr+/BTSb33dk/iQv21dbhC5rs+s9vsiNUSai7neN+eT+xRacconf2r0PaNgNLX6Jv8WJkPo+/tpbKc88jOOJ4jER+9266HvlvnGSC8jPOpPz0M960SrB0HAYWv0Bi6dLRj3HRpk8IgfD5CE6cSGjKVEJTpqKHw2/KGMawL44osltlUHuIyG6no7i6xxkju0cy3pIGtYEdYKX2s9HT377u4Ry1cjt4ez/VXRjli08Mb2Mwda24iL2ruELTQfOjdBPQvIryIFG2817V2cp6a3c/1mpvFZy85ykbqoDKqRAohb7NEN+JEiZs3Qh+HaI1yI4WsB1EqARVKKDQwQhCJIpWGkNuXIp0dYyqEgiFSa7YwN8zOZZ12GSVQa5gkbMlWcshW7BJZ/P0xvvo6e0lOdqP/Gvwmfefzo8+eCpaasDTDVeNh64OqKwDIwLt21Azj8Pdk0Gt/gvU1GGWl8Lk+WDnKby4BNLd+Opr0U79AKIk4GmWe+LIHWvQJs8DoZBbV3rJeYbpWZDFahCxGiit9ghavB0Vb0OlBxufFPLVZ7E6+zCqKsh359DsJOgaTiKP2dAEuSRKN1H+EKKsBv8p56OtewIn4+K2bEVU1iE7dnkWboFywtffjIz3Yj39ABT6SW3YSWjWTPRYJUqCk87g9nbjppKex7PpQwQCGGXl+Orr0f1AIQO5jKdBx3OQwBj0ni6edIVLEMGIF5CBQLouYudqpBHCat6GXt+A2z+AW7DQMnHsrI2IVaK2r0eESzDLYzDzeDTTh5IS2duN29MB2TRkkoQm1xG44ecU1r2C7I8TOu1clG2TW7GEwtaNOB1tuO070YREVxZWfABRN4FCIo1yHCbc89BeFT/luvTdezuOhFx3H41fuNHzLT4CkXruKXK9Pay5/3csuPGr7Ln1u8hdO5j80JPs/PX9lM6dy7j3jh58caDoe+IxhM+PvX0jHffcxZqUy9QSkwV//htmzRuPIX4rkd+zh95nn6HQ3j7qdjeTQfP7qbnk3VhdnfS98DxGSSnVl1yCuZ/4X7Oi4k2XRshCgVzLDrLbtpHdvg2Zzf2ftR4rPfFEyk8/85Dt/0giu7+uMQ8p2b2qyx4ju0cy3hKy29+Myg+87uPEkPZ1RIVWG71iO2R5tZ/7i2ZOXhzwYIVWusPSh8FKmGt51V63AK7lRcnCMGEeek5xGfRshRFa4uLrH1bRroBUB+ST3tjtvCfBUC7K1SAPtK2HGYugYxNK+KFqEqKiCs0fRAVinvyhfxdy1TO4vf0QKsEIuuR3dOObVIcWqfaifYUGqhjjLK29iH7BsoknM3T1p7jh9od5Ye22UUfZlbo3AAAgAElEQVT7/lNnc9/HzsRv+iAYhmAlaEEI2OBvhK1LEOddi7tuBdazf0QbPx5fQx2iaSYYYfKP/wEhwCwNop19NfgtRNkUT9qwYw1y+2q0hhkQiTH0mY3w2RVlNUUP3mEopXB+/RWs5u1o42px2uOofA5/QzXZra24RgihmwjdhFzKi6aWEmrqiR7VgNXei0oPIAyJjCdQQkG4hvD1N6MyafKP/hKVaCezewAtUoIWDKLHytEryjCCPjTpuX+oXAbh80M4BlUNYASRhQIql/Wirfczz0QwiAhF0SJR0DTsjWswSw2MU95L4aVnUZkkem0dhRf+imZnMM65nPzTj1DYthWpwBcrQaufgDZuMvgCQyegdvNGjJ5mop/6OsasE0ncfzuRi69AL927OUk5NoXmLWSeewrnhcfwnf1uNJ+fgSf+iINJzfU3EVl40l7PSTz+31jdXSS2bGf8pz5DYELTgU33txBKKXpu/Q67duwidtJpWM8+hd6xHaIVNH7vNvo3bKR/+XJm3HQTejD4hl+n/babqXj3++j++S30L1nMyt1xjp85kdorPkR4/kKCM2btVyLwdoGTydD1yCOkN20kNHkyFWefw8CLi3HT6X0eq5TC7u1FSYkRjVC68ARKjl2AfoSeFI1hX4yR3bc/xsjuQUB1vuJpcEfDCJKqGNTVFpvTYPjvkallquiHq0ZIDXjN9r2qvW/mmxlF4jA4lsMJ5YKvBCpmQ99ObzyxcbD1Sa86HSqHVxZDdT24jmeNO2E+9O2C7ID3eN2TZahsHNnegSSEcBIQDOPs6cV/ykJEuKL4egAaKB0QoPatvuTSKa666Uc8smTtqEM+fWYDf/7CxQRKo9BwNCT6waeAKCrZiwgHESd+APXknWRXvooeC+M/9nhERR2qbjqF+36EqJ2Ake9CVNQjZs1Bm3o2wvCjlELtafb032rEPChGHau+jmGtcuV4tIbpUFqF+9z9OC8/gysFmlJYGYVu2JghHRWtQZSUQ6rf08XGqlGuS27FSlQwTHThPKxNmxBlNai+3ahU2jtMgVLCV3wCFYiSe/JhDLsfgtHiCZSCQNhbDBPluMhU2pPeAMIfQK+pQSuNofkMQHqShb0cRUb5AndtZNV0CsuXYEZ1zLOvxO3tIv/cnzGbJmIveQqZzxP4wKfJP/oLZGcnUgrPvxcX4Q9gHHUMvtMugEiMnusuJTK+lJIfPopMp0g+fB+xj3x6v9Ox+9oLcKVG+LyLcdp341twEvGHH0DoOrU33ISvceLQY7MrXiLz8hJytpdKV3/tx9H240d7uFDYvpXM8iUs+8UvOfnOe9j5+esI2Em0hinELn4foQUnsPX732fSdddRMnPmG3oNadvs/tZNjL/pm2z/4HtI9sbZuKePGdObMB0bv88kOGEigaaJhI85DrP28FV8hWHgnzT1X3LVyGzbRud/P4ybzY5O4pXCV11N1fkX4CsvJ7H8ZRKrViHzeYRhUDJvHqUnnIivouJfeCdjOJQ4oshune/Qkt0Oa4zsHsl4KyZjYcMryIH46BsHK6eDNmJ72YkV71fOAXNJUfQsHZIlaIOxtsWAiJH3/y+6dCX8JprfQqcPdIEwQ5DLer650kFoYVR/EuItMGkhpNsQ4WooH+8RL7dok2TnoXkZpNI43UlEdT2ifzeFtn4CpxyHiFaOoFaDKXb7+XCUi5NOccMdT/Jfj/591Idcf9ECbr7ydJh4NGQLkLegphR6Mqi+dsTc4xAlk5BLHsLq6EP1daNNm41v4VnQsZb8ulb08RPRRQ72bEGUlCDO+3f0ivoDOm5KSejdg9y9GZXoAeUi1z2H3ZtCr63C6kgicikC845GZVPIVAIp/V4V1dAhFYdAiPyOPbgIgrNnodpa0KbOQ7asQSUTKM1AlNQQes+VqFSc/PKlQycXIhhCFC3ERKQUrboevW48wnwN2RNaUX9r7hWHqmwLlU1DLoXKplHZFCqXRqUTaKl21LwLsF96BiOsY553FWga2Qd+geYXuJtexdXD+BobUaWV2Mue89weFCgziMpn0KQDPj/aOe8n/4fbCZxxAaUfvZHc0ufBNAku2LtSO4jkT28iu3QJ/pPOQSstQ7mK2NWfIvHME8Tvv5uKD32M0nOH/Xat1hYGHvo1oXMvpuuB31N50SWULhp934cLvb/4MU44yoZnn2diUxO6lcXdvAZX6PiPPp7667/EjjvuxCgpoeq00/BXV2PGYgd1Gd7q6qTrV3dR/4WvsOWcE8mGwrT253EQWLk8kVCAyliYkHLRDmNTrAD8oQCR6bMILzie6KlnYhb9lN9M5Nvb6fnLk+RaW9ECASpOP53ShSeAUqRWr2Zg2UvYfV7SY2jqVEoXLPCcVkaBr7LqX6q8j+HgMUZ23/4YI7sHAXvx3cj0fuyrBiu7I5u/hqq9I2J9D1gq4FXuhiuv7oi/R0oYihXa0QflySF0v7eYQW8xgmCGEGaQNxpWcUigFCqfxY334Pb1INNJhNOPHhVoWJgVEUSwDFVaB0seh1knQ7oX6hq9JrF8DqzBhDsF8VYwDFQuh9OeQC8Bu6Mffdx4jMbG4uPEXqtRPxfpgJtH5fL88O/NfPUn9+/zkIBp0PxfH6O2rhb8McSkk1CtyxD105Bb1yOCQbSj5iM3vgpOBnsggdvSCuFS9LoGjKYmnM4ksn072viJ6Ok2RH4AwhVoJ74b8mlUx3ZUb1uxOW2kZhtv3oVKvSVSBr17UK1rsbr60CeOw2luQxYK+GfOxZhzIqprJ6q3FWW7uNKPSvQj8v2oijqsrTtRQT8iEMTERl94Lu7LT0ImgzT8iKomwp/+DsI4sEvRKptGdrQgO3Z6hBavEUelE6jkgNfQls+CbaPsAsop2lppGkiJXl2HEQ1A/QyceBxfWQjjvKvAdcn89udobgrZH8fJufhqKwle9SWUK7GWP4/14l9RmSToBjKX9yzNpsxBbXgJ38UfJ3Lh+0n86ieUXP5RtHBkn7Hba16g7wffgooawuddgrVzO2XXfRmhabjpFO1f/RxaMET9f96GVqwOuskE8V/cRullV5FYsYLMhg2Mu+7T+CorD+h4HWq4yQR9v72H1uUrKbnwvViPPEhk4gRMWSC7egWi6SjGXX8jVt4iuWEDhe5u7IEBL058EPv5zjAiEabecANCCJJLl1BobSF6xjk0X/pOfJEQZiSEVl5BIp1n9/ZWsjnrgOfRoYDrutiupDwWpTpk4kcSrB9HeOYcfOMbRn2OWV1D9OQz3rAUw83l6HvheQaWLUM5DtE5c6k891zMsjKUUmSbt5FctQo5SkAFSmF1d+Pm80N3+WtqCE2bRqC+/n9V4eNgYFZUHtLK+JFEdu+v91NrHBoHk05H8uE9hTGyeyTjLXFjaF0J2f59NwymlNkZr8HKtYARIRCiuB6EGLQRKzo4aK/R9g4SYm0kUTa9Bh7NHCavxuASRJiBofv+P3vnHWZXWW//z7vL6WV6n8wkmWTSSC8koXekqCAginq9oNivXCsKinhRf+r1eq2IojSlCYIogtQAAUlCep0kM5lM76eX3d7fH/vMJCETIWAgerOe5zz7zJy999ntnLP2913ftYSiFTbLQVo51xvYSLpTM+O6N1hZt0ltLKziKIGiQflMRFHDfvshsdfeQerJ5/BNm4ynYTKybRfYacSMsyGfQpTWQ9kERGCf/tJ+/pcQ74Z0Cidcj9O+E4SB2RWHQKRQKVcKvFGC4yB0Da2iDKUoilAKPxzSASeNCHghluSubj9X/ed1WPaBFanPnLeYH/zbKVAzC+rmwq5XIArkAsjO7SizlkC4ErnyAZi+EHvTS0g1ip2xINWHMv0ktKZZmGufg3Qc8iOoFWUous8NX6iaDGV1KJaBjPVCfGCMgEjLhGwC0jFXL2vbkMtiD/RjKxrCdK3EpGXhP/EsCARRBDgjvdC/B3wBzN4RRC4OdZPJb9yKOnES+V278FUUo53ybuynfofM5pCeAKJqEsGP3zBWnZVSQjqBM9yHHO7HGezCMXI43Xtx+rtdz9hRz+mC7EEoYuzwj8lqxKtuPhwbxwY0P95zL8bpaMfubEWrb8Rz8SeR2SzpW7+LaqeR5XUYr7yM74RT8J33QYRvX0qV2b6T7J3/i93RijJtAcrAbkwliDJpNqELLyPzxB/HvGOFx4tW24A+YRKqRzD4hasQE2egBIMooSiB087DO23W2LpHHryH4XvvpPorNxKYs2DsfAz98scEFi3FM2U6nT/7MVokSmjOXAJTmtErKt7Wm8z4Q/cifH7+9stbOeHnv6T/V78gtfpFqq/4IMO3/C8UVxC94GIiy05AKy1HDYdf1/aOrF1Lz8MPM/2GGxBC0PebWwjOX4Sntp74i88Te+F5jLbdOMkRdK8HPRRAeztdBCRI26S3d5COzj4MoaF7vVSVhAn6xg97ENkM/lCA6JKllFx0Ob6mqW/87aUktWkTg0/8FTMWQy8tpfyccwk2N7+u4y2lxOjtJb2zhXxv7xvejn92BKdMJTJv3hFb/zGy+8+PY2T3MCBju5GHsh47bAhGQxCwjFc9CtGp0gZ7tKpbqHZJXLI8GlwhXpVdP7Z6ceB7jT7Gmue0/aajcggVRvXGbxcUQLPdmwZfMSJUBUWNyEQ7cvdTZNe14OQUAue+A/HiQ4izP4lSNW1scZd0DUOsC6ekDh75NoSLwJFYvWmEnkLR/UihFSq2TuFYutV2x7SxRxLYyQxIULw6anEQYZqolWWIkiikTb78yGa+/+t7Dth0n0dj50+uprp5LihelKkn4mx+ElFXj7NxLXh8iOnHwd69kOyD6YuQL/0FMXkuxtbNEK1FRMrANECAk4ijmMOQToA3WEjcK3DFgiftaLOaUDU3utfjA48X0b4GrWkW9u7NmPEc2oRyrL2DOKaN1jgFUT4BHIkQNqpXdTXPiUHMlIWwcjBpJvlVq/GeeArpp5/CV16Mdt4V2I/8CgwD2xdBLalGFJdBPoeTy0Emhcyl3Shg4V7frsuYROx/g6fsfx0qbv+kVRilcPbTjjtu86UQEnQNJ2fiu+Ia8BdhPnE3it+D78NfQyZGSP3oOrSyImRRHca6l9DrG9EaJuM5+QKUIreiKi2TxOffh51OIRefjnfnCygfuoHcM48Rvuj96DVuJU8aeczOPVjtreS3rMPesQ7LUdAqqlBLKlBKK4i+7yMHnHujo53ub3wJb/MMKj5+DWrI9eONPXg3QtOIXngJRn8/mZbtZFp2YA70jzXOaaEwemkZaiSCFomgRaKF51E3XeswPo9SOtiJBObQEObIMNbQEOaw+7zsgncRbHY/K9K2GfifmzAcydaWDpbfdBN2Vye7v3gNNR++kuxfHsCMxVBKKsDjBa8PNRBA+AIofj/C5x+XjGklpegzZtP36KNM+9rXAOj45vVUf+oa9JJ9lTcnlyO1cQOxF1aQ7+t73fv3j4Y0DHLte1CNDJ6AD08kSDaTZU9rJ6nsOJVVwLYllqpRUxqlRINQYwNFp5xJ0QUXoYYjb2p7jIEBBh5/nMzOFleudtAGFz4XQqAVFRGcOpXg1Gb8jY1jIwvH8I/H0UR276z1UaUfIbJrOnygK3eM7B7NeCsuRnvzSuTQIe6edS/4/AhvALyFqS+A8PrBGwDdO6Z5k2PNZ/vJEcaev+p/zvjzSadAkm0D7JxLmu1CSppd0Am/GmP5FIWKpCgMhUtZMHb/O7rVtwqODbYEEYJIpECMNNAD0LcRsgmstCSztgV/yEFrqEeZdTYy2Q9WHic+gNXVhdnVgzZpCp6JNdC6EWwLOe0E7Cf/gDZ7JqKy0SW5BUJFgUgetDmpNGZ7J3ZHO3pFGYoqUSqKGJDFTL7kC2TyxgHzf+b8RfzgynPAX4Jy5mdwVt4HnizULESuuAcxbQkiFESuXwHNsyE2gtO1F2XJO6BtDVZoKrK/G2nbSMfGGepF5BOgqW7l2edDBIPutSUokEMNx1HAtJD5HOSyyPZN+N75QZyV92NlBUp1CfaeHlfiEI7ixIZRQkXoJ5wHloPdsRuldxOKYmMOJRBI5JQFGC8+i++8i8k+8ScUBbyXfRz7gZ+AZSOLKlwHDBw3ttgywBp1tBAFbbmGY1hgjcZfOwXzDx3h1REe16JM+PwInw80HcUXcivvwSLQdOyVD2EOJt0bANNGO/5ctDnLsR77DWrTHDznXIHd20H2x9ein3gWTiyJk4hjjQzDyAAg0WfMx3vGRZhb15B/+E4sKfA3N5Fv3UP0W7eRXfG4G8AgBJ7JU/HOWYQaKSLzzF8wNj5HbtVq/KedT37LOvSZCyi+8jMHXyvpFP3/8y2MkRHKr/wE/hnHAZB++QVyG9dS8uFPjNsIZaWSWENDWIkEViKOXZhaiQRyvKHsvwchUCMR9JJS9OIStNLCNBql9etfZeJ1N6CGXLlGZt1qjNadpFu2s3M4Q+NZZ1N/0kns/MRVCF2n/LRTyW/dgDU4gB2Pubpq03K9jy3L9Xr1elGLSvBOasI3ZwGW6SBUDVFRTf+TT9L81a9iJ5N0fvsbFJ1xNqH5C9GKSw5vn94C5Lu7GHrsUWIvvoDZ1YHXp6EeQqagaArC52PHtlaGY0m84Qh1UR8hXTlAh74PEk9pOYGJk/HPnktwwRI89Q1vurJvjoyQbtlBekcL2fY97ijP/1EZQ/Hy5ZSdfsYRW/8xsvvPj2Nk9zAgbXsfUXw1TMMdPs5nXLKRz7ghAIUpRm6fHRgwnjZUCAEeX4Ek70+a95FoPP7D9muU0imQ4lF7MgNpZfaTNRTIsnMIkvyWQbp6VF8xGAk3KMNRIFAEmBAsg651oHqRTpjMxh3I9s0QLkGm02AaKJpAKwqhFYUwhuJYRc34g8OoPtfD1U77oG+bG8csC+85BtctQ/i8KEURhN83tl1WMouxdTee6dOhvwulNMi1T3Xy/V/de8Ae+DwaO3/+CaprJyImL0FUTMHZ8Bgi6kEOZpDD3YgZc6F/wK2mHrcMtq3DyedQ6ichgmUox52FzGYwN6/C7mxDZpKMyl2EooKq7JO5CMAxEdIEbFAFqBrGM4/jnzcbMnGsoRi2KUGaKNEytPln4OSzOJ0t2O27wLZRp85FqZqI2PIkSsCH1dsL3gByyiLM5/+Kd/lJmC3bMPv6Cbz3Y9iP3IK0HXd7wE1FEyAte+xUKrqKEg2jlFaiVtYja5pQyhuQ4RKwLORIPzI2APFBZGIEmY4jjaxLmEe/lhwLpbQSeluwsjlIZrFzBpTUoU+fi7BzKNES9LPeh/HSE+QfuY3gZ25CphLYm1ehLzsHGYySe+BXGOtfxnv++zGffMANwahvRk92IOefS/jSK93NdhzM1hZy61fhJBOoVbVYa57C2L4Nfe7xaLUN5Fu2UfSRz6LXNhx8BTsO8T/+nvhjfyS49GRKP3gVQgiMPbvHpANqcQmexiY8EyejlVe+ZXIGo6+Prl/8lIlfu3HsfwM/+g7hcy4k/tB99PmKSSdTLP7iFxl68H66f/EztLJyNwrbH0ANhVDDbvVZ8flwLAsnHsPs7cLs6cYeGULJp4m8672UvvM9ZGMJBp59luZrr0UaBpnNG0mtewUrNgJS4q2rJ7RgMZ7q19eEeUSgqaj+wAH/ckyT5JrVGL094y6S3rGdxMoVeKSJr7aGRNZgx9qNGI4YPw1OSiI11VSUl6IlhtFsC184hH/KVPxTpxFauBRf84w35QhxDEcWRxPZvavef0TJ7hUd2WNk92jG0XAxvllIxwYjt48kv5o05zIF0vzqU3YI26Z/OrhD2ELPIDQJZVMQmgLpHsjFoOEU2LvCrRJ6yqGoFOv5x13eV1ELE+Yg6o9DBFxjd+c3n0DOPI3sS88iE0kCU6pg2gJkaw8g3er4aBXcsd33d6Sb8qUq+8IPfH5EbgBL82Fu2oF33nHIjg6GbJMpX7qDdP7AII7PnL+IH/zHhyCXRDnvi8jVjyBlGtFwHM7jd0F5LcpxpyJffAAmTnV12N17sEw/ip5FW3oJorzxTR3JxFcuRwtqeJadhfX8n7EcHbU8hJO00ZtmIkcGobQKddkFyEwC85Fbsfr70Zedh7LjGZRgEKu7G4rKMb3l0NWKPqEamTfJtezCe/o7UTY/5Ub56oVqbHkdor4ZJVoOkTLMWJz8qhVYuzYjs2nG0tKEQCiK++Ou6QhVLTzcv/clrDlgmTipBFZ8BOGYqEE/qlcH08I2BHpNFZ53fBDZuRP9rMvJ/OQr2IP9+E46B3X5edirn4ZsCv2kC7EzKVLf/BT+j19H7qffwEqmCJ52KpmVLxL82PV4Zs07qDIX/90vcVo3YrS2IirrCJ9/KanHH0af3EzRhz55yONvjQzR/9//hTE0RN3/c/W6UNCfx4Yx2nZj7NmF1d83NrLimzYT/7zFqIV5jwRGVjyD2d9HxSXvBcDs7SZ27+2UfOQzxH57Kxndz441G1j0+c8Tqa/HyWWx02nseAxzZAhrZAR7ZGT85img+9Zb0HJxQmecT/0Xv0ps02aGXnyR5i8dHBlsdHaQemU15kD/Edvf14Jj5HHSKRSfn/DS5YTmLXxdpFNKSeLlv9F7+62Yu7bhjUYIzV8wroxBSknn2rXsXrUWbyDAzIsvQoYijGzYgDLQg5rP4fV6CEyajG9SE6GFS/DPnofiHd+H9xgpfutxNPCLY2T3zeEY2T0MyDE/2qMMQrhWZf8ikPksTm8bcu9LYKeQdgBBHKVqEmgaDLeDJwhqFNF0PCJU6y4nJVgZyA5Cdhhnw/Mw3IlYfD7WuqfIrNqE3jQJ34VXISLViHFuEKSUyOE+ZPtW5KgPrS+I3LsBRc3jePwYW3bjnTMdOTLCV+95iu8/caD/rldX2fm7G6nxaFA/FWXGWTirHkYELSibi/PEr2HhqYjBIRjZCzOWuMlrJZXYg8PI3RvQL/uiSxrfIHKP3o751B/wv+ffsJ/7PTZe0FTskTTapBl4zrsChnqxXnkamY4jispw0kmsPW1os5ag9G1B0VXM3l6onoTRM4JeUoIqU0jNQ25PJzQfjxKO4ow6KsQHkOk4mAZCgBoNo5dXoUSiCF0DI4ewTfccerwIj9e9sfD4XDnDfl35QuBGV+se7F2bsJI5ZGIQp78DK+9gZ3Mojo0jdTxNzfgvvhKnZR3qye8m9+MvItGQlo3w+lGnzYf0MNqk6RgbV+PEE8hcCruzDcu08VcXYxdPQqmoRdoWWkU13rlLUMursPp7Sd7+P5idndipBKFzL0ZmM6RXraTqh3cgxtNV7ofkc0/R96PvUnH1Z4mcee4h55OWRX7HVjJrX8ZJxkHV8E2bhV434bCGpoWioFXXuVrfQ6Djh/9N6XkXEJjiNlblW3cSu/8uSj50tRum8crL7BxMUTpjJhNOOw1/aenr1oPauRzrli8gFNTxnXw2DTd8i8Hnn2do5UomXn013qPEkeLVsLMZki+tJL3uFaRt4Z3QiLduwrjz6pVV+Kfsa0qzUyl677ub+F8f3U/Gsx+kg0zG8VVWojc0svelvzE0MExF81SmnH4KIhAiaUkG12+A7g6UVAKPKsav+EuJ4vfjq67BU1GJXlmNd9IUvBMno1dW/Z+VMQjdc0Q9rY8qsjsheGTJ7t70MbJ7NOMtcWNYcQsycyjrMc1NpNLcQAN0X2FaiEJV1CP3PVTISz8UhFDc7fH43ake2O9vv+t5epR+STqWCXtW4AzuglwaZdEHYet9gIZUyhHlpQhveGz/hR4Efyl4Qji9G+DJu6D5eESuD5nOYezpwnQCrlXXAUEah6iOS4kw8yjZJGp5KQo5pD+Aubcf34zJDPZ00vS520jnrQMW+/QFS/ifH/0CNv4ZZellyO42pMwiQj6cnbugZzvivM/C83dCZQ2irAG59WU4/SOw41mszRtQFrwDbc7yN3RuHNsi9fmL8C87ATncgT0Yw85LPI3lOFVTsXa0gqrjOfsytCnHYW9djbP6MWQ2idE7gFbbiCYTYJvY/b04FZOwBmLoU2agjLQjjTyOUdAQKypSOvtVZgUCgQgGUYtLETWTESXViGg5BKJIRUHkMq4XbiYxNiWTQuazB+6IkUdpmoUYaMXctglpOYhEJyCwM1mMeJb8YApP0wyiV/0H9rY1aKdfir36Kcy/PY4oqQR/EKe/FzuZxnv6BWTvvQX/x68n95OvY6fTeObPweoeRqbSRK79HlJCfv3L2AO9aFV15DatQSaGMXdswbf8DNSiEmS4mMyKxym/4YdjzWiHPBdGns4vfRpraBDvhEb0+gY8EyailZWjBkMooTCemroDKnbSssht34zVM35k7aEgHRuzq2NM66sWFeOdNgvf1Olj1mrSstj9lS8y8Rs3jfm1Orkcw7/5Gb4Zx+Gdfhwjt99Mun4qseEY2aEhV8IF+z5nh7gmw/X1TDvvPDaccQKRmnLC77yMmk9+lsS2bfQ//jj5IdenPFBfT8myZURmzPiHx+j+I5Brb8PsG78/I7+3nezOFrwTGig5/11o0deuxJvDQ/Tc8RtGnn0WNeijZP58RrZsZGjbNrRAgPqzziFQHEUgkJqG6Q/jHILsZuIJEq2t2H29iGQMJZ9DtS0UVTlqv8ePNILHn8jE62587RnfII6R3X9+HCO7hwGn7QVkZpxQCQmYeVdjauT2NeqMhkz8vYqwEEipFFzIBFJ1rcWEp0CUdVeni7fw0L0uqRaH4ZognQPdHsyCftcs/O1Yr72OtwqODWUToW4hir5vGM/ZfD/OYDtEJqJoGTctLVAO3lrE1OUIoSAzcRjqQI50QT4FZcXIjSvByCAapkA2j+zaDVMXI8rrwBtCaK6Nm1Q1DvKuHYWUOC0rMJ56AiMLmmKilYWxYnl8dWG+et+LfPfRtQcs4tVUdv7+B9ToDhSXIWafj1zzJ0RQQtU8nL897FpjeQKQ7IUp86B2Pjzzazj304jtT2GnLJx4Gl4w6MsAACAASURBVP3EC1CKKw77UCa/fClqURC9eRbW1vU4FmhTZqLoEiXsRZY3YL70ErbwozTORI2GEYl+7B1rMGNptGgRWkhH5jM4g/3ktSJU1YM2fR5Kph851AXegKvNDYYhWooIRhGaW2GRlokTH0HGR1z3CM3janttqxB/7TZqCukUSNSoTtfe97AsZKQCdd5pqOWVmI/dgR2LIYwk5NM4eQfpD5LtTuCYktAlH0A3E+inXIwIhDDXPY/xzIMQiCAVFburA9/57yP7wO145izGWPUsViJO0cWXYFfPJH3r99GbZxP+9NcBSP3hTpTyavLPPYKxZTPqpGYCZ1yA3jgVs7+H+G0/pezL30avb3zN82ElE+R37SC/awdG6y7sRBxpGO5QeioJqobwevHU1uFvnol3SjOKP3DY1Tqh6wiPF8Xrw8mmybftxmjZhvB6Kb78wwDkuzrpuf3XNH7lawdeM08/Tr5lK8UfvJrko3/AGuw/6P2Fx4NeUYVeNwG9dgJqafkYyWp58EGklFRPbKTlA5cQmTSB8k9/ieKzDqxqZzo6GH7xRRJbt76t1chREl+yeDHlp56KFjrYa/lQyLW3MfzIQ9jpFNETTiG8dPlrEncpJbEXnqP3ztswevsIH78U34JFbP+f76LEhiifPp3KJYtRg6FDHhfp2JDLH/C6DIYx/8UChg4HgSnTiMxfdMTWfzSR3d82ho4o2X3/ntQxsns042i4GA8X0rZcm7HCQ+ZSLonLxl2ylk+BmQPbdOcdayAbxzVBjH7RKfuI8P62YozaihWmb7fF2HiQEuk4CJkBsm5jXvU0qJsHIx3Q9QLOwBBiwhJEbIPrK2wVuTcDAIEooqQOimtB1ZDrHkSGArD6GaiqRygm0jDB9gBKwR0At/Kue0H3FI7Pq+BY0DAHnBTW4/die0Lkt+9ybXo9OgldYfpND5AyDrxp+MTpc/nhlz+LkuxENM6EYDUyn0RoKWR/Ctm5BdEwF/p3ub63s05xG+3aNyKmLUH2tULjYuwdGwtNagceK+ELIMLFiHBR4VEM/tDYD27m7h9irXmWwPmXYL34F6Q/ipNJ41l+LsLvR+7diHLqFdC2Fmv7WoyUQK+fiFY3GfPJuzCTedSiUjxhDSeTwhkZIBMXeEtK0abPQZ9zPKKsGpnJIOODyOF+N8CBUckPYFs4Q/3Yg73ua7ZZaLLTCgEsWsF/91UVQznaWGlBchi1fiLa/NPQFp6G/czdGKueRRhJpD+CjMdRGqaQ7xrCMmyUmkbkUA+oKmpdE0q4CJGNIbevIj+cIHTeJZjrVqItPgPzqT8g00lsrw9/00S8H/wKmTt/jLllLcF//zz6lBmkHv8D5saXMPe0IUoqiH7gE9j93QTfcQlm114Gv/Ulou+/msCyU974pW9ZOOkUdiJObncL2c0bMfbsdsnY4XxMpQTbRkrQy8rQSktd7adlYvX3ETr1bEInnQ7A0F//gpPLU37huw5YhdnTxfAdt1B82YfwNE466C0cI4/V14PZ1YHZuRdreHDsZl6vrmP7tt1MPPdcaNlO5zeuJdDQQMMPf4G/acobPj5HEtK2GV61ioFnnnEr/WVlVJ51FoHGxnHnV7zeA2Qd0rZJvLCC5Kq/ja+nlRJpmfiaphI95fQxCzZjYICe235F7IXn0UrLqLz8Ctpe+hu7//wnIiE/3vH8h6UkUlpMSWU54doaNL8fiUQoKuIoi6Z+K+Ftnklg8ZFLKjwa+MUxsvvmcIzsHgZkcrCQXjUOhLLvx1vdLwyiEO17JDW1LmnOucTYzIKZc7vajTTkky6Zdqx9D3t/0kyBeOj7Ut7eThJcVAelU2GwE9nfjhxuQ+RjyFAJSmkImUtgxwKoxVk3m6PxJJSKmQesQkqJTPfD3i3gs5HrVrrRwyURMICqJph8IqJQ0RVmDjJxyCTGd9uwDJytK1AWvQvHGMF++JcojQ2YHV3kt7ch/B6++cI2/vuF7Qcs5tVUdtz9bWqMBCJSjjJ1LrKrDcIeROkkZMvLoIWhvw38XmiYiTL7HTjP3wFF9QjVhFQSseg9B6VMSSkhl0GmYshkrDAdcRPKpEQUV6A0zSZ941X4l52Is3crlNZgdXSizZyF7OkGXxCFLMq8M1BnnIjx4I8wRuLoFTXoS87EvPv7GIks2oTJaHYCJ5vBHhkmb3nRfQH0xSeiBMIQjLrxvgO92AM9yPiwO8IhQATDqCVFqNEi0DWQuLrdwgiDdKyC9Mfjuk0IpaC9LljoWSZOJomxuw21tAJtznI8Z78fp3s3uZ9+CRGNIB0VkjH0D11L5o6fo02b4+qsjSwiM4RSUYfnzEsBSP/XR8l0D1Ny0y9If/8L+N/zEXL3/QIp3BsXJeDBbliI77j5ZO/+OUplDUr9FJy+VnJ7e3D6uwhdeDmYBuH3XQ2AnUkz+PXP4pu/hOjlV/3DPgpvBk4+R35XC9mtmzB6usC2IZ9DCCi69AN4JzYB0P69b1NxyXvxN048YHlpWcQe+C12PHbwygs/GYrfj147YV+FNxQm/dJz5HfvZP2aTSy9/nr6bv4psfvvxNc4kdCykwktPp7wgsWo4b8v/Xg7kR8YoP+JJ8h2jS8hMWMxFI+Huve+l3Bz8+tap5SS3O5dxJ95EmtkGDUSIXryafinzQApia14lp67bsMcHCS67ESKzju/MNr0KjgOw9u30/Piiwxs3jyWpKY4NgH/4Xky/yuh+tTTmfPla4/Y+o8msvu7SeEjSnbf15o8RnaPZrwlMoaWp13CexAKHq0FyYL7dSNAijE+KUcT0gopaeM1R70+jJ6uQpjEmyWoUu4jwLb5tksapBGHcBFK7WyomDZW6bOf+CGEihBeE6kX4fR0oYQVhD8CE0+DWDtkBgq+ubYrIfGVQCbhVghbNkHEjwiWuDcsoxWYA1LsRsM3Xr1RDhI/5AyUJRfjxLuwH/01akUZZIaQls3QSIqmL/+O1KucGT56wnH87Nb/xdn0MuTzKLOWwkgvoq4KubfTDcCI1CA7NyMiRYjm4xGNi3Ge+Dmi6XiwEhAfgWilezMzFtla8Ef2+N0mPd+Bw6/GY3fhOecKkl++FCUSQK+pwunvR1n+HqxXVoCVRw17EMIdXZCGhbL8PTjd7ZidbegVZWhLL8D87Xcx4mn0hgkoiT6kaZHrGUSrrEEars8qloEI+FAiEZSg1202G5UrmHmkLwQoyHQSKz4Cpkti3Zs0e99NmNw/QU2MPVc8HtTGKZg7tuLkHbTps/Fe8SXMW7+A1daOWlmFPTwMqsDzse+RuflbKMVlqNX1KOXVyHQCp2UtIFDnHE/+sXsxcgr+ufNw4nGIDUK0FBLDYOcRio0ZqkeEIjjrVhL95s1kfvdDTBHGfOlJPAuWozdMInDGhagl5YVLxGHov78OjkPp5298zca1txpSSvp++B3swT60SISyj38ONRLFMQzabvwaFRe9h/D8hYe1TieTxuzqwOhsx+zci5NJg2XhO24e6Z072LR+G6f+4Afs+NhVyK2vEGhswDZtrGwWoeko/gCe8nLU16F5PVIQQuCpqsY/aw7eqdPRqmpfl+7VSqXouOceUi0tFC9YQM27331YDVJWPEZ8xTNkt28BoRBasIjIshOw4gm6f30LidWrD4xnHoV0UxDVSAStuARvTQ2h4+bgb2oCn///LNn1FBfjLS4+Yus/Rnb/+XGM7B4GjPXPIhMDh3jVlRBIWRiSdUajrhyE44ActbiyQNp/t6FsbI2Kuk+GMEZqC8+FG3W7L2r1XwNCGCipTpA5qGxEmbgQSiYhe7fjrPkjorYMYRkw6/3IjbeBFkB4AoVj5AHLQJp2YTg3ifBNQPoM2LIFvBrCLxD1S5Flk/ZZXEHhPFnja6ulg1z/eyRB0AMoSy7B2fUS9itPIYxhl9xJyXUPreE7D798wKKKgD9/5J2c8YFLEZkMdm87Cg6UlCMq65DxPpTKGchX/oSsbkRYJsop/waJfuSmZ6FyAtTOQIQrCsEkBV9bKcGxkdkktL4MlomYuABR7HqWGk/cg37qxWRuvh5nzw78J5+CuWE13g9/BaV2BjKXxVj3PM6O9ZAaRjhxFCuHmLIAJ1yLuWMT3klVKBNmkP/T3TjJJNqSM2Hr89jZJLk9A4TmTUHxB91RDcfe15zpOG4oQk8/ViyBnTcKIXUKwudxY6BVteBJul+6n3TcCqRTOH8CkDZWLINeHMC3cBEiMUy+tQO1tBTPWRdh/vkO8EWQmgKDfYiJM/BfdSN2RyvG+r/h9HYis2mUolLXvi+bQNgZjI5enInHoXZsxXv5pzAf+jUEI0hNRxnuRSmK4pRMIPvCU4T+/bOYq56C0nqyjz+IqKyj/Ns3k7z7l65+d8LksfMdv+c3ZFc/T+nnb0SvPrp+LKRp0vm1L+ApL0U4DuXXXIdQVaSU9PzmV0jLouaqq99Uw5jRuZfYvbcTWLyMwTVr6ErkWHrttez41Mcw2nYhs2lUXUMLBvBNmIC2X6ra2wLTxOgfcJsmFQWBdJ0OqqsOaQGnhqP45y9Gq52AEIKRNWvofugh1ynhEIS39IQTKD/11HGPrbQsUuvWkHjxBWQ+h6duApETTkINjB+jrEaiGL09ZHftJL11C5nduzCHh7HTKf41LCgPHyWnnU7dJz59xNZ/NJHduyeFqfIcmZvpXsPm8mNk9+jGW5Kg1rEKmUuM84qroRXsI7guAXX2q8S9AYy6BYxW8QpNPXI0DGHszP2dL7jRWFZlVKdbeC7cx1HVvSsl0lKxEyZOdzfKcAtCOKB7URadj5LtxBnei4gEEHXLkb2v4AwPglLlklUVhE8FDbDSrnyj6WxE71pkXkBnC/gFYsopEChGjEY1225E8yE/Co7txvW2vgBKFHwRxPGX4qz6PXLb88h0DhzJUDZP8389SDJ7YKpaY3GI5z7+bkqWnIYvYIHP7wYqBALI3S0u6S6pQfS1QKQIUVKOdu412Bv+BEP9UFyMCETdSk82BZmkK7kwc+DYCE8AWTbR1XobGUT1VBzpRfjD2L0d5O6/Gd+8uTgdO9GWnI52+ocP3L1kHOPlp3BWPYIWUCBahmxYgrltPf5TT0HGusg/twIyKdTj5iB3bcXOZMl2xVACPlcj6tiuRKGwTqFrqMURPGVFCE1HZrNu9K89ekNRcL9QFJf4qhpC94LXC14/iscL/oBrl7Z9PcZQklzXMHo0jHfx8TgtmwEFvTKIFbdQK6txhvuQiTieC/4d/fhzD9g/c9NqjE2vIHt2oy0/B+f5BzEJYmsB1MwI0et/TObX38Xp60LUTYHuHQh/kPxQEpnPEX7flRgt20g98SgogrIbfoTW0ETqgdvwTJuDd/a+qmhuwxrid/ycwGnnEnrHxUfVZ8yKjdD5hU9QdP67sQf7Kb3yU2OvpTasp+/e31H/2c/jqTj8hshRGJ3txO67k8DC42l99C+os+Yx84orAFcCkN3bTmrNKuJP/5Vc6+6xJrG3BYX4XSUQINDYiK+2FsXncyUEnvHt2+x4HHJp/LU1aKEQngkT8c9fglpeOf57OA79Tz7JwNNP462qYsIVV+AtP7StYH5vO4mXXhg3PU9K6YZy2KM3hAJPbR2+SU14qqr/z1Z21UgULfLmopr/Ho6R3X9+HCO7hwFnz1pIjlPZlbz56qrA1c2qmhsyUHguRv9XmEpF25fSU+hHc0nD+I0RSMslc44FzmiKmunGCjv7JVUdNdhvm/UAGHlkYgC7ZRuidjZKRIN0D8LjhxnvQm6+ByE8LiEVhdhjxU0Xk7k00tIQgUoIemHLBtAdRGkpeKPue43JS/avMo6zTbaEsknITX+C4inueZlzFvKRb7kJeShI0+A7f9nE9b9/4aA1fGDxDG7+jyswB3Mo9ghqMIw2Yx5qAMhmwFMMiUHsPZuRiRgiWol60iUQb0dksmOaVsKliGglFFW6DXlC4CQHoX0NMjUMmh/yeRjYix2ZhLbwdNLf+QSKT0OL+MEfRj/pfET9HETowNhWY+PLWH+8Ga3YB4qOVToJp6ef4Me/jrPrFbKP3ofIxdGmHYezYz0OAnwBFI/HjRce6520EaMjGYqKCIYRxVWI2iZEef1YsAS2jcinkNkU5DMHyhhGkc8gi4pxNj2PMziIORgj1x9HmzwLdWgPanEUvaIMcziDWhTCGehBCh1RUY3n1EvRmufvO4uWSeI7n0NVHdSGSRgbVqGf/X6Sv/05/oUn4DvpHKzWrVgvP4NS04iIdWENDmIMJyn90e9I/Pwm8vEsTus2ghdeTqSg2U3/9SGEx0vglH0E2+rvYeTm7yPCEYo+8HG0sjdOHv/RyLXtpvu6/6Ts3z4Ktk3knAvHXrNSSTr++7sUnXIaxSef+obfY5Tw+hcuYf2tt9H0qc9SvejIdcu/GUjLIv704/Tf+Ruy7e3YuTxqKHxor2JFEJw8GU9VDRKBr7ER3aPiDA+5YTQHvYFE2jaBhUuRFbV03n03xvAwleeeS9lJJ72pmyFp2xhdnWR373xbwznebvinTiM0d/5rz/gGcVSR3abokSW7u+LHyO7RjKPhYnwzGOs83187a5v7/h6bjjoz7Pc/yyg0nO3/pVk4rboPfCGENwS+EBSmo/ZQRxtkqgc5shsq5iDsLCQ6kbkUsnsTxvZ+9LmLEFYHpNOIpVfD7kfdYxGugfKZiED5WJOTXP0zN762bBkivQWZ9bnOB17hWruNVsjH3CsOMXzrOC6R9kahbg7ylYdg0jKEBDncAd1bAJe8Wd4AJ994Ny9v7zxoNb973+lc+vO7kSvvx8pa2AO9OJ4ATuduhK8YUTsFryeDEhLI/m7wlCC9AXfYP1zubuuoFMYuTIPFKHVTUWongy8IiV5k7zbk+mewo814zrmC9Pc+jTPQjXf6VJxEEm3GQkTAD34/IhBxiW/Q1bsZf3sC69n70aqjkLWwLB2JRuCTN2GvfYLc048iFAettNh1PBCam3KnaKB73PVFyxETmlFCEddFIta376bKH0T4w+CPuM4RgTD4w+ANHPSj79gmJGM4m55FqazE2fESciiG3dmOlUiTy2nowkAviyCqJiPzeZTcMI7Ukdm0e45DEfQ5J6AuvxBFUTD3tJD51f9DndCEGNqDFUvj+8A1xH/4dURFHXplFWpxCXLvDoTmQfa1YSQNvO+5Cm2oDTNQSeaeX6I2NFH+3VvHtjW3+gWsrj0E3/n+sf1wshliv/4R0rbxzZpP4NRzjpoqb+LJvzDy+98SOfFUfHMX4p8554DX+++/h2xbK+H5C9HLytDLytFLy8Z8eV8PjI49xH7/W3zzF/HKT29GbZhM0bRp1C5bRklz81Hprwtg9vUyfP9dGJ17x33dGhwgvacdI5FCLSoi1DwNvaISxetD6Pq4y2glpQgjhxwZQC8rJ3jymQxva2Hwuefw19Ux4Yor8JSUjLvsMbz9OBr4xTGy++ZwjOz+C0NK6Q5151OQcx9y9Lk9XiOadKvIb6fuSyiu0NUaAdUH3nLQdGT/WojFyO/sx7toBjIfR6gh1KX/fshVORvuQGZiOGkPIuxFVM+AtU9DtMKtjuo+NwRBCJfMSmfcYUDp2DCwDWFZIHRkzVTY+AxMWQ5D3dC9ya26Wm4lc5cMsuCj3zsoRrgs5ONv17yXxq/8BJ76GdgKYtnF0P480lcBXR3kRDnWyodQSyN4K8sQvjBK/WQkEow8wrHBdpCW6boZJAeR2QxS6gVlgEB4fIh0F07dUvST30XugZux1r+AVl+NSKdQlpyPUlSMGOlx9z8cQtg5RNNSRLSK/EO/wNq1Ba0igBIoIbe1Ba22Gt9FH8Pa+CLGmpdQQgG0ximIkio3NEI6yIF2SAwBAlQVUVqLqGiA4iqkdLB3rMdq2YiMDyFTcdeKLJdF2tZ+EpLR4y9dYigURDCIVlaGdvKZiF0v46Ql9p6t2H0DGFkbFYk2eyEim3WVOskBqGtG5nI43W1u6EUoiN4wDe3Cj5G+40fQsQO1aTr2tldQZy/Hd+7lZG/9FkYOpC+E3dmKSMfweE2snIoTLSd69mlk9/STfuwPoGlU3vIQyn66SmPXNnIvPk348o+OkR7pOCTvv931HE6nED4/WmUN+oSJ6BMmuv7EbxN6vvsNFJ8fPRxGq6ohcu47D3jdGBwkt6cVc2gIc3AQc3AAJ1dwozkEaXfyBg1f/ipKYf9HCW/4rAvIrl9DfE8b/bvbSPQPIEJhfDV1hBoajuh+/j2oXh/1Z59D0cSJrz3zfpCOQ3LFkwzdcwfplhby8QR6WQXCd/DNgHQcfHX1lJxzHjKbIb+3HWPPLuzYCEowhGfmHBJ7OnAUjeqLL6Z0+RsLkjmGI4ejgV+Mkd2pRVQfIbLbY9hc3hL7l+RRx8juYUDGWl23gH8iCMUD6n4PxeP606oeUA5MThttejrIw/etxFiTkoVM9UByL0SmIHc8CzIOvgnkN23HM6sEMTiAOOUzKH5XqyWNHLJ/L7KvDRJDiIapMPg3pOLDGVFRKj0wlHcroKX1rmPAmAPDq8nWfrBNZP8uhJqFXA6kgoyUQE87aAGIdbhabbuw/WW1/OqRZ/nYLx4/aFXnzJjA/Z/7FP6Z9TAyAuEIYvIM5LonUZZ/GLn2UVh+GeYd15EfSCAdFU9DI3j8OLksMp3GSaeQZr7QqCggEHUlKpaBCJeB6kPd8xKec96HUtqAk0qQf/hXCGHjnTQB6S/GyebBG0aUViFUiXAshE9FmX0mBIox7v0BVlcneokHZdJs0o/9Bf8pp6NVlmO27cFs70CLRlBr60HREGW1UF4PCGT/Huydm7A69yATBTcMIVAiRajV9YhoOUppJUpZNVTUowbD4/uT4t5oGH+9B2PjKkiMoE6biacqiMwp2Bufw4zlkdKCvECbtwRME6HaKNIBoSB1L+gBnLatYORQlpyN9+z3k7j+oyglURRdYO7ZQ/jGX6NoGvk//BIr76DPXYadN0j88Ct4iouRqg/f3LkoM44n/psfI1MJPFNnUnTNDajRfRU5q7+H9MO/I3zZlSiRorH/p1f8FXPXNvRJzSjBINIwsXo7cdKpsXm0qhr0iVPQGya73rhvAdo+fAnlH78GIR0yq1ZSeuWnDiDwh4vsnjZ6br2FiTf815gjhbG3jZG7byN4/Il4m5rRqt14b7O7g/Sm9aQ7Ov4h+/JGYKXTdK1aTSoWR6+qofGii5lw5llovtd//KWUpFauYOTBu3HG0dniOOTa28n2DyCCYYLzFlDzic8SnDoVs7uT2B/uIdfWiq1oZIdiZLu70YJBlENsgxYKEZwxi+iS4wlMbUYrPlYRPtI4Rnb/+XGM7B4GZKrP9bE92iAUl7xqhYhizbvPr9QxCzrd/H6aXfchHeO11/0WQ2hBKG4a8yWWtonsWwepFHKkBZImFDeSf/lJPPObYe8QoqrgdamqEIwgvBrSzCD3tiOqFIRlYhuVKEo/TDwd0dcKZfvn3hdIvn0IDbNtIrt2QHERwkpCfBgUD9LOw0jM1ellRhiVMlBcjtQF7/rGPfx51baDVvfjy0/jQ7Nn4jtuEkomgbjw87D2Tph8JsIfRb7yZ+TS98B91yPrp2MOZBBCIMqqUSsbENWNKNEyhBBu1bm7Bdm5DWmZCGEjdZ3E3XcROuUkZKAO7fizyP70qziJIfzLTkT4g24RWLqaP2d4CEfxulLx6kqU485CphJY657DWPcSekURYvZSsvfegf+MM9CqajBWPoOjR9x+zFg/GFm3gU64DWdKUTF6QyNKVRWKpo/5ApNLu8dJFKzzxm62DlHJUhRY+l5E2wbs3naMDWtx4sMIHTweCzuVwxhJoIU8WCmBPnex2+iWi6E4BkTK3AbAVAw7m0d2t+L/+l1YOzeT++2PUCpqEPkEVv8Awa/+GDUQxvjT7ciiKpyhfoyX/kKuqx+togq1oorINd8k9uObMDe+gtLQhJAOxZ/7JlpF9dgmO9n0uE4N0jAwuzuwOloxO/ciTcO93hQFraoWNVqEk89hde5FGnkOkNgcBoInn40+4fVVKu10ir0f+wCeyVMo/eBHSTz4OyIXXIyveeZrL3wIZHa20Hf3XTRed8OYVMHJZTF27yS/ewdmT9fY50wtKkZ9Gx0ZhKbjnz0ftaiE9PrV7LnvHnq3bsdRdbSionGX8VdWMudLXyFQenjbnd26icE7f0Vm0wZyQyNIr5/g3AVU/ftHCc46DnPXdtIvPI00TZSGJrdP41WQSMxMnnRbK+mtW3CGh5BGDhCuxOT/aEW46ORTqf/4p157xjeIo4ns3tNcfETJ7nt3jBwju0cz3hKymxlyZQFHG6Tjklkr74ZLWHl32PsQEEJxq7u6z52OkWSfS5rfxu9LaSQhtgsRrkeEasb+73SsRO7d4rosRCbh6BGsF/+IPrsZJdQImICFROCMDGN3d6BGowi/D8VrQMkkrA3rUOcej+KvdRufzJwb82zl3ejkUYeAV8M2kbYB2SRUV7lWcr27wRNC9uyGaC0MtboSEMN0XQQaFtK36TnmfukOBkYOTD8LeDRe/tZnaYh146kpQz3+bJSqWuSGx1HOuMaVm6z+I3LeOfCHm+CE96I0LYVMHDnSCyPdEO9H2gZC9yOqm6CmGeENIIe7kW3ryT/3ENZQHO8JF+I55/1kbrkBp6sVpbwE3xXXIjc/C45EShuh+ZBCYG1+BbW2HqW0GOW4s3HWPYNTVEf+j7eil4YRJ1xG/oFf4Fu0EOrqcV5+Bnx+N3ihpAyhe0D3I3U/Ihl3HSeS+0IJRDCCDEZf5Wm8LzFt3OsuNYJs34J2+Q3ItvXIwU4IFmPFujAe+xN6TTlOMoM5OIBWWoQVs/GedZHb8GabyJ6dCGmjLjgdZ882rM2rUOacgP/S/yD5kxsRyT5EOIwoqsTcsArvJVfjnb8c40+3o845gfRtP8DqbMMyBYEzz0Ora8TJW6R++wuobkAt02GNTQAAIABJREFUKsHp66ToP2/E07AfsbVt0g/dhdbQhG/h3092ko6D1dOB2dqCubfNlagASiDsEsHDJDB2bBgnESd8waWopYfu+h97fymJ3XcXsb88jH/eIvRoBDUUIXrx+97wcHp6y2YG/vgHGr583d9dhzUyhDNecMVbBCefJ7t+NfbgAMLjxb9gCf7Z87ATceyh8TzVIbZhHZtu+w14fMy//uuUzFtweO9pGCSffZL4X/9EbvdO8iMxbKESmD2fiss/QGT+AvLbNyNz4xRWpMQa7Mfq7XLtLQEUgVpehfQfOmL4Xx3ehon4p7y+gI83gqOJ7N47vfSIkt3Ltg0dI7tHM94SN4aX74VY72vPKBQ3tEAZdVLY56awz3HhyJnOC6G4RNYTAI/PdTXw+EH3g8ePFArCMQ4gx2NTe5T0vQ2QuNVSCXh9IBxEyTSEN4pMdCLbViPzfZCwEXPOwX7lAaz2HkQ4isxk3S96zYNSVYta34Tx9ENo9VNQqxWE0HCKl0DHM2in/udhb5qz9s/I5AAgoNiH8JXB7tXI2CDoIcgOg667ZBegeQkM7eWRFzfy7pvuOWh9i+bO5NnrP4rcvBrVK9Cv/H+InhXIvjjKaVdBNolc9TBywhR44b7CzYjunk9fEHwFb+FcEjQfQuhIPYjQPIiqydgv3U9q1QZ8512B94QLMJ64D3PTiwhdR2gCpaISdcY8lPgIBCLIfBayGayebtTqapSiCGLG6TgrH0ZOXoz5x1+ihrzYNXOgZxee+lrUZe90wyH6O5CDnW7jJBKh6oiyOkRVIxSVAwIZG8DpbnMJsJED63WGl1gGQs/BUD/amVciu1uQPa0waS7WKw9jrtuCGg5gpVLInIFWWoJl+dyQDVVB6CpKKISIdyE8AexMEpJx/NfdDtIhedNnUEJBPFNngKJhrF2JmDIf/4VXYD33EMbQMGxcQXogh2fqNISmUfS5b9N39UV4Jk/F6O1Dnz4Ha/Maij55Ld5ZB3aEZ5//K058hMB5lx42cXTSSezY8GEtI3M5MiseRy0px06nEKpC+ILLUIKh11zWTsQZvOV/ybW3o/9/9s47PI7y3v6fd9r2VW+2bMmW3HsBTLFNN52QYgLkAiGUkFANKfcmN0Bu2i/JTW5CbsiNQ0kjQIAkGExoLoC7jXtvkmXZllVX26e9vz9mJVuxDKa4hPg8zzy72p19p61mz3znfM8pr0DBxehXhVpQlJsK0XKPSiD4nuPFV66gfc7r9Jvx1X8KDaqbyZB+ZwnptSu97+dhms3UcJTIhZeSjSd454FvktjTyNDp06n+/C3eBd/7gNW8n47ZfyO9bjVW4y4yzS3YDgRGjEHpzUpLStS8fEJDhxGsHUSgphZF17H3NmK3Hs4D/uMPrbTsqPpanyS7//w4SXaPAqRjeXGpphfdi5n2bvPmHjHTXhxqz0/9w985D1LDI6hC93c/7zFpxiE/JNJ1DizXyi3PSuXWKeWt3wkKES1D9hmOSOxDdtSD2eER94rTYO8K2LcVmYhB+QiEZuO27YWiapTiSk/D6phgxiEbQzbvIrt8O3ptEUrUj6g5F2fpLJSxV6BWjgRyLhjdUg+LwxF9aUSRbz0JZgL6DkVYTZA/ELn4z4AB0jwQhuA4UNYfUTwYuX0ht/3+bR59adEhY/7sf37K7RPKMf/+PGpZOWLgaNQ8GyEjKGdd4xHeJX+FAYNBuJ4UIJuCRCckY57frj8IkWJIxRGqgowUIWwXd/Vc7OZmskYloQs+gTRNsnOeRREKvlsfgrr1WEtewe1sAV1HMVTUogJk1kIKDTUUhGgQkdcXkgkcJYi9bA5CU7GdIPrQkWjhICIYRhSWQl4BAgdpppD76nGb9uDG2ru3VYnmI0rLUfLykcIjFqRzGuRUCplMItNpz1tUCG9bHQdpZpEtLfiH9EHk9UOtGQ+JFtxd6yGkYG6sw21uQnEzmJ1pVJ+KVlyGGDEJbeQU7M1rsNatwG3eDalO9P59kK3NiEGjCd74TTLzXsKc+xdEOA+tsh8iFMXesBzKB6OPmIDcthJ75RtkUwpma4zQpNPwX3ET6TmzyKxajl5SSraxEd+kKZiL5xL93O0EJ1/Q4zibW9aRWTKfyNU3ew4RxwDm9s0kX5uFVl2LvacBraSM8MVXHRI73RvS61bR8dwfkaqBEo7gplKeXjyTzkllDk9chetQ8V8/QQ17jXexRQuJr1hG5R13f2TbdrxhtzQTf3UWdst+AqPH4zv1TNb99Cc0vT0ftRftuXQlhs9g+DXXUHz5VSi9NbFJSXrdajrnvoq1by9OS9NhPNolbtbETqWxEgnsTBYlGEErK0ctKvmnuKg4GsifPIWSKz951MY/EfhF1zo8M7yYCt9RIrtZh+kbWk4IHvVR4yTZPYEhHbsHWe4i0NI66G8rp+0DDmm08gUhEPEidQMR8Hs2T6K3zPUTAFJKaN+NbFznJYX1HwfhYmS8EfYuRRQOxq1bD24rxCXi9Gtg+ywoGOhVVrOJXIOd4mVxkEa6frKLVmFM6I8SKkbm1SK3vO1565LbW0JFoCKF5skk/UFEcR9EON+rFksXhAmRQciVL3tey2POQ7RuQO5cB6mst28TLV611bYgWghnXgcrZ5OItzLh3plsb+xZeYlGwmx68TGKK4eSmfltfGdfgVu/FqErqMX9UC69HZGTNGDkfiBD+Yi8Us9nN1Lk6WU3zUN27PF+HANRyCuF9W/jWibJ7W0EzroQfcoVZJ74PlqffjjNeyFUCPllqJUDUAIGztoFOHVb0WsH4rbHUAYMQwgX4RPIpIUydALWpnXITYuRKFgtcfwXT0MNRXH378eNx71GNc2HKK9CqawBXwhnTz1Ow07c9lZPh5pOeP66iuKJhjUd4fN5k+EHTev+wRa+AMIfwN65FmvNckQ0hD7yLLTycoSQyG1LIK8Ac8Nm3HgCYabIpl18+UGPUCgCdcK56OdcjVBUsoteI/vUw6iVfaGjlcDX/w8RzifxP/+JkGmEP4jMZND6DcSp34g67hys1YtRmrdhtmdwVD9KUTHKgBHkf/4uEs/8huS8V9AKi8m2tBI84xwyC14jdN7lRKb3DO5wWppI/v15739P09AqKtEqq9Eq+nuRy0cJmZVLSC2ej1EzFDfWTvQzNxzR56Rt0zn7r1h7d/e4Ne65SqS8C+pe4JomqdUr6feTX+HrXw1A+/y5JNetpeKGm1DD711h/meBlJLM2pUk356D0A1Ck89FjfSeupayHdY9/HPi69dSOaSW6umfJXTqmb02ZrrZDInFb3tBLL0s025uwslJP9xMBifWgZuMI9M5Pfy/IEKnnUnx528/auOfCPziJNn9cDhJdt8HpGMdPjxC0bqjXE8ESOl6VcB03LvVnY570bLpuOfP2gO5E6QR8AjycawOiLIaREGFp6dtWI2MNyMK+iIzjVAxEfauhNhu5P5WxIjzIdMMdhqi/aG4BhHIP+BzuupRz6ZLFOHs3YCaH0aM/gy0bgIj6jV4ScDybLw83ymJTCVx9+9Bdno/KCIcQamoRPTtD1kDuWcTdDbDiFMQ615HtreBLx+SzV4CWDoDhg8xdBLSklC/koUtKaZ8+eeHbO910z/J73/wNewFfyMbU1CK+iDaNqEYQS9+dOx5aGOmHFFF0E12wPrXkPu2QdMupObDTmTIJHzk//vPSD/9MIqTxrjmfuSOVbj165D4cBwNGWtFuGnc+s1oVf1xm1vQTr3Ak7voEnfDCtQr7yS7YA5i+yIc6cdu7UAtqYBwPiKQ6+A3M5BsR6Y8xwihayihIIriIpBeoIRueMTW8Hl+z92Jfr187ywT5bTLkK5L9snvIxUFJw5aXhQtT0Hx4aW9Lfw7bpNnpWZlpRefXNIf3WzxeuYmX4lxznQ6vzIdJRpCMdOIvgMI3v4D7F3byb75MrJ9L8JKIjEQuoa007gpB8XuxN7ThIyUoJSXYjs+8m74Elp5XzKL5xH/7S8QwRC2KzCGjcJavxKtsIiC//gRSi+3taXlOTHYu+tw9jYcSMrSNLTKKvSqQajlfT8yH1rpuiTfeIn00rcouPU+tMMlfR3peO/yk5Fa/BaJhfNIvrOC6LTLKLrmRoSuk1izmva5b+CkkiAEwUGDiU48FV//qo9FNdJNJUkuehM3nerlTYnVWA+KSnDSZBp37KL+L89hZJLUnDaBwgsvxT9y7AfeD04ygVm3A6u56UNuxT8vjMoq/LWDj9r4JxTZHVFMhe/oFKz2Zm2mrz9Jdk9oHBOy27QR0odppnAsj2C+B4Sqe/pL1dftnPCP0/EgzVJKr1qcTXH8NLsS2bgROvYhBoxHVAzyXm7fjdy+AArzwZ+PbNgGdhvYAdSpNx92OHfj85DYD4G+uGYKOndB6SC04Z/ILc7NJctZB4I8/mHbpZTIlr04c59EGXk6SmUlNDR6+t1oCSLTgKzfBkoAnJSn1bZykdHFFYhRFyI3LgAnwW2PvMyjsw5NV5vz2A+YeunVyFceQZx3M3Yqg/X6E4iUJ61Qasei+HtqJCUSshlEtAAlvxgRzu+eCOXhPvZlpB4E6RDf0k70nu9hr5iHs301vsnTUEafi1AUZHM97qZFYAQwd+9HUUycbevRKvvitrWhnX4RiuGHwgrcJX9D9K8lu3EbanwPlA3ymuRSsVzYhet5JKs6QlURgYCnL1Y1MEK4lus13WTSSNv05pdu7rO9VwplNo3IdOK75b8gGcf62/+gjB6NvbEOa90qfDWlaNNuwd24DHPZfNxkEq1mBFppCW79etwBp2InbVg3FyEdlMtuwXrml2h9KqCzncCMn6MUV+A01pGZ9xJKeT+ct/+KUlqJ296K09aCVjMYe90K3Pw+CCsDYybjtrVQeOc3ALB319Hxw68hHYksKEWEwpBJItuaybv7AXy1w47s629Z2I11WHXbcJoac2Emyvsq1knTJDBlGvpBzXJdaP+//wYkBbfdf+QDfgBkt2yk/anf4lom+EPkXXQ5oQmnHlhH1yW9bSudS5eQaajvtig7HpCOg9A08iadQfS001GMoxe242azJBfOI7N+DUooBENGseWNuSQ3bqCivIiykcOJnDMN34Dao7YOJ/HBcJLs/vPjmJLdjo4OvvGNb7BgwQIKCgqYMWMGl19++SHzPfzww/zqV7/COOjE88ILL9CvX7/Djn0ifBnfC54VmJ1rCDv8JOWBH37hBbEeMpZQ9IMIstcFj+bPBSX4PcLxT1oxka4LO99B7tuK6DMUqsfA5vlIpxkGngfb3kSaLdCwD+WCLyNCvftMuuk22PQ8CB0sBWm34XamUcZfhxJ97y71HmNZMdxZv4Q+tSiDhsCWjRBvgsI8aNgK6CCcnKxE8chzQTmidhKyown2bqFFUxl+w49oiyV6jD1i0ADeWTAHdcN8aG5AOf8WZGY/JPbgtNjYC1/2Oq97VOSFFx9tZsCxvRhpXwSp6EhHoll70cuKEY6JFagm25EheNa5oGg4m1Ygm3aijj4TffKVHuntbMFZOguzJYUWVLA3r0UtKwXLRhs7GRGMIjMpKKwAO07mhT+hB0BE8rxoZgRCSmTOv9m1bGQy1b2qSiTqSQCiUU+6cKQERzpY27dgb9+BduZlKCVluG/9Ef0TX8Ka9xeyb8/Hd/rpaBUDcRu2kVm9HJHz/VWCflQ7jgiGUT/3AOZrT2Mvehnp6iCkV20uKiV4r1dxd5NxMn/7Pcbp55F5/v8gk8BpbUH0rUE0bsKyDbBd8r/5Y5p/9v8IX/IpQlOnAeAk43R8+26cjnbU0adj79gE4TCisx3/aVOJTL/pmCSGScch+dIzKIEggfOv6HEOsPfvo33mT4h+6t/wDR11VNfD2r+P5p9+F7WsAqNmKJlN6wmOm0hwzAT0khMnOhk8EhpbvJDOJYuQpoWvspKCc8/H3//ohV04iTiJua9g1u9EyS+g1dXYvWIlAStN/+q++EtKetX2AmhFJehVAzGqBhx2npP4aHEi8IuudfjzqJKjSnY/s7b5hOZRHxTHlOzOmDED13X57ne/y8aNG7ntttt46qmnGDRoUI/5Hn74Yerr6/nxj398xGMfk8pupt2z+DrOkFJ6IQZ2rrHKzvno2uYB0txrQtpBULWeZLmr2qzqx9e+JliCYni3xKWUsGczsm4VlA2E1g1Qcxok98P+OmSiFUL9UE/9DDLdCR17kG0NyHgrdO5HDJkMsbWeREGEkIaAjj3YMR/SUfFYmH7QpPVaRJOug0wnMU6bgFw9F1cNovbrD61t0LQZVBsytjdGusOTMmRzFmSBEGLiJ5Br54NMMnP+Jm7//hOHLONH99/MfT94BPeln4JUUC68FVk3B0qHIfRCMHIOA4eB27Ibd92byOYGZCaDuWEtgYGlSECOu4zk354jcvu3cLatwZhyOa5tY89/DmfDMkSfWvTJl6P4DJwlszBbkmh5PuwNq1CiUa/7vnYshPKR9RtQp05HSkH2Bc9+CdXwKrnBMCIURYQjqKV9EYWlH/qCS9pZ3OV/QTY34YoQjqVAfD+qmkQ7+xLsWU+S2dqA/9JrUfKjOHOfxWrtQOQXIVQDadlgZ1B1iX75raT/8FMYfgbu0rkQDqNIk8Dt30PtP7j7WGdefga1ogp331as15/HDRajOR2Y7WmkHiRyyx2og0+j5dsz8I0YR951t+Y+69L+4//A3rwOY9qnsDaswYnHUH0GIhwl74Y70Pr0OyYXoeamtWQWzSH86RtRDtKRxp56FGtPA0X3PnDU18NNJtj37a+i9xtA8W33kNmykeTK5d2uAXppGcGxE9HL+7zHSEcPQlFQonk99kWmYRftc9/A3Le318+46TRGn76UXXMdWvjDJ+DZLfuJv/EydvN+bF+A+oYmsukMitbLBaErEZZJKOQnrKtECqJouu5Jnvz/uj67/pFjCE48/aiNf5Ls/vPjmJHdVCrFqaeeyqxZsxiQi2b8yle+QllZGfff3/O22glLdrOd4B5/svth0XuFOUeUDxescKyQbveIvK8gV51WIJCP3LkG/AGIaIjSMcgdS5FuBzQ0QThX2fWFIZCHCOchVWDTYhh7JmL/JoiUQUcLkpRnyavkZAFCeLfehfCiZnV/ztbr4JOJRKp5ZJesRh9aiTCzYLuITBs070YURpCtLYAOVhJCQbA1r2EuLx9GTPNCJ3auwg1rnHnnL1m2cVePzQ4H/WzcsIG+UR/um38E3Y+Ycg00vIkYeD7C14sN0WEgM0lS370JX3kYAmGoHIq5aRtOxUgM1cS47MZuDbB0HdyVr2GtWoQy7HTPRmvJS1jtWbQ8HWv9OwjNwBgxBlFUCf4Qcm8d2vmf+6BHuPd1ltKrimeSyHTCa2RLJ5DZFGLAcOTaV5AdcZTaU5D5FWR//z2UsjL06lrsZa9iUoqeZ6BVVGBv2YjV1II0QigBH0phIbJ5HyRjKKech7N8DqgBpKojMp2IgkJCX/t1j/Uxl72J27IPc85TOBkHo1855rZ6lD41qEVRwnd+HyklbT99CGnbFH3lv7oJU/y5J0jPegr99AuQ2bSnpYx3oJWWIwJBRCQf4Q+gBsNo/arR+w1ALa34yCu/bjJO4s+P4ztlMr4R47zX0inaHv4egVPOJHTOxR/p8nqDtG32futetJJySu74Sg9rLqtpL6lVK7BajqNllmPjxA5KxdRUjL798VUPRO3N+gvQCoqwOjpoevpJhKZTft31GGUfTgfdBXPXThJzXsFNJ3PR7f8AKXEti6zjkrIlsVgCk64AIfef9m7eh0Xl5MlUT5t21MY/kcjus2PKjirZ/fTqpo8l2T1mbfl1dXUoitJNdAGGDh3KsmXLep1/7ty5nHrqqZSUlHDddddx7bXXHqtVPTyy7V7owQkOoWgHRQPrPR8VHeUEPyFKJwux7Z6GN1KFsEwIbYOMCYkksqrAi9ntTCEr+iBKBuPZL+QS4zSBcC2koUIaL1nN8hLkhD8AbhoCvgONUZDTRipgmciOfTkNqUAYAfD5ESKB/5wzyC5YjhpIow09HdxKTxcqQHFsjyCrGli5qroQnn53/auICVci99Wh2HF+8bXrmfT57/Zo9EmkMtz3pS/w9EtzoHY87FyHXPwCYsho5J6lyIPIrlD1XBXeB3oIfHk5r92ci4E/hFJSgR1rQgsXIvbvQC3tgx1vQzn7EsxXn0IpLEM79TyEpqNOuAhl+Flk//AD7KIytInTYMnL2GmBPmI8zqaVmCuXYkwpQrgOIhDE3bAIZfiRVVKkbSFbduPu24ls3p0L8jC9RyfXHOg63nHSDI8QabnJdXDXzEW54N8Q61/HrVuLMr4C3z0/I/uTL2EGo+iRKLpQsDuzkNyKNmIiWvFO3EQnthvErt8NoShKwMZdPsdrwvSFkHv3Qn4+tLdib1iMNnxS9zobp0zBbtiO+dYLiFQ7SrQIZD34fVhbtuQOr6BoxoN0PvM4+792KyXf+QWK4SPyqRsxaobS+cvvIwvLMKoHoeSNIfX2GwhFePs8EkUWFWM37CCdzYCqIqIFCJ8frbg0Fxtc+6Gie5VQhMgNd5KeO5vElnWErrgGJRDEP+YU0ssXEjj97KN+G1xoGhXf/Tmtj/w3u++80ZMoSe91ragYX+0Q9OLjJ2sQuo/gpVehFRUDnm7abNxFtm4H5q66Xj9jNjYgzSzlV1+DEonS9OQfsGMxiq/4BHpxca+fMUrLjki6Y/QfQOGNX3zXeaSUOK0tmPU7KKvfgX1wY9oJfm4/Wgjkf3xcPk7i6OCYkd1UKkUk0vOWTyQSIZlMHjLvxRdfzPTp0ykuLmb16tXcddddRKNRLrvssmO1ur1CZlogE3vvGY8zpACvTHlwSlUXsZLId+t4Od4nS9UPwTJE/hCQNiK2w6v0FlZC/Savoal5HUQrkB0dCCMJiuM116U7AcUL0RAawu9D7lqHrChEmHHoMwqaNnrVTtfxSJZ0PD2sdLsr2l17QOIik+3QngWfgdBC+M4+A+ud9Zhv/RXjE3cjCssRSJBZ3HTa486WCT4fIlLi2ZTpUa9a2X84bFvGhKp8vnjJKTzy0tIem/7M7Lnc/OLznH/Jlbid+6GtBdmwE1FaBNlctLOUSNK59bW94xXMB+keMJ0TCvrwEZjzGrx5shba1Ctw5zxDev6rRD53O27rPszZv0epqEabMBURCGNcfS/Z334f5ep70CZegFz8Mq7MRx82DnvTO2Tnv4x/2qe9atjWFYi+gxB5OZIgXUh2IhPtkOjAbarz0t6SMTAziGAUwvkoRRXe80AEEYhAMGeHpx/qNiFdBzJp3MbNuLNnIiZfBVsX4y7/O+o512AMH4LdmcFsz6BFwTfpUsy3XsBdtxZ18DAUezu666CdNw2nbjN2q4rIJBDREtzmBpRoAFc1wIXsM79AXB9GHTiye/lavxqUonJkMoYdSyA0BbdpN9Lq6VMdnf55tP4Dabr3Rkof+hlqcSm+sZPIu+87xGf+N+aaZWhDRhK94mr0wSM8z+N1KzG3rMdpb/Wa9SwLoXje2qaqIrMZL1RO1Q76Rh4BBGgDhpB/4x2oudvzwXMvxarfTvLFZwhfeS2h8y8ju24l8ReeJm/6jUc+9geEEILiL/W8e+emU2S3biK9egXZ7VuO+jocDjKbIfbCM2Bb6P0HEL34E553bvWhDX4Hw02laH/hz5i7Gyi84BJ8g4fR+veXcFa908vMLtk9XuJZaMRICs49Dy3au0XZkUAIgVZcglZcQnDCaR94nJP454QQ4qhV8D/OdwaOGdkNBoMkEj0bcxKJBKHQoZWL2toD3ajjx4/n+uuv55VXXjnuZJfWVmRif+/vdVUGldwk1AOVw4MfFbX3+XLzCNXw3Bi6xhPqPzz+4+u5MRUjF/WregTOMXMuA9mDQhNMcM13tQ46bk4MXRAqJPci0/tBjyAiVV7F1FoN+QXQug8698CgSxHNO0ANQLbT87mNFnuVVYkXopEIQcaC1hgEc69ZFpQN8TxenRzZlV1kt6cjQPe/vesg2xuRLdsRGRtj/AisvAiZ330XY/I5iPVvQzSCIrJe8pKdBr8faVkIBNgSNr6B6DcWaQQh08G375jOs2+vpznW82LvzjvuYNW6s/ENPwd321LYV4/U/ShVNZ6zgc9LxRO5irTMxmH/RqSZQkTKoagWFIHSusvbpGwapEQUVSAyCfwXfpr404+ihMIEzv0ExFoxZz2BOmAY6qjT8X32bjJP/gT/zQ+hn3IB1oKXcEoGog0ZB+uWkpnzAsbUK1AKy7Bffdzzb84mEdkUUtU80moEEJF8lMETEaVVkOeZ3ct0CrdlL26yE7e5BdmxAdnZnktUMw9M0qv+0XVSzyuDSBX8bSayoA9C78Cd/ShKeSnCjKEMGIi9YT1qWyO+UWOwLQcn6WDtiyM7WxHbd6FUDkAvLye7dxdGfC/4Ah5Bb29Bqa7C3dNA+tHv4P/CN9EOIrxq1RDc3VtwYx0okRBOLAGqgd2yD624vHu+4KSp6BX92P/NOyj48tfxjxqPMWQUeV/6OvE/PIJdt5UskuzqJV5lMxRB69MXo2ogakVftMoBCFXFbqjD3lOPa9u4nR3e8Xs/ZNfK4jbupPmBu1CCYYJnTyNw2hT0qhqyKxdhN+1BK+tD6JyLSLzxInZz04e2IvsgUAJBAqPHExg9/r1nPgbosg6LPfckLb/8MWo4ipJf0Ou8ekkZ0Ss+TdFnb0DaNrHXZtPx9xcIjh6P1mdQr58pOPMs9H79SW/Zwt4nHsOJx9GLiii44EKCNb1/5iRO4iQ+OhwzsltdXY3jONTV1VFdXQ3Apk2behDbd8OJ4JAm86vAyO/tnQOEyc2Rpm5bJddL1HJdkBbI7IF5/nF+10FKN2dhltteeaAi22NZB//d/ZrrEV9Nzzk0dN0O1g/ov4Sa06MqILTc1PVcPb5XdlJCJABKBvQIWAlkx1ZvffWNro5mAAAgAElEQVSgF98pDOiIQbLJI0Gt7Yia06CtwdNldqUO+cNIVUGYCWTRMGRqJyLVBn2Gw+5N0KPZS+RkHjq9EgupIPL6QKwRmd2PXNOKPmw8ijENc8EbaG4MtbgE4p2A5lVbVc+7lmAUkUmDEUVie2ETLTEK8nV+cP05fOHhF3ssanP9Xr5w7Wf47dPPoYTyYVAxsn4DMjEPmeqEbPKg5sOD/ycEUlEA2zvehaWIcBi3M4FakA+b5iIKy1FSbUQ/dztORxvpubNxkwkCZ5wL6Rj24lfQT78I/xU3kX3sv/B/6fvok6Zhvj0bt3YC6lATuX451rI30IacgjbyLE92kF/qEVrDD4BrW8iGzdjb1+Euet2rakvpRRWHowi/DyUYRuQVQdUARDDifU+77kBYGWQqAYk2ZDIG0kEdPhlpfAbnmR8ilQDStDCXrUIpLwVVRSvNw1q/DFk9Ek22oV79LYQQuDtXYS99GWd3PdbOLR5hTSQQVUORe+tRAipOSwyjdgh2Qx3pXz2A74ovYJx1CQDamDOwF72ME+vAqB2A074ZtW8F6T/9isidD/Y4dnrVQEp//Bit3/sqiZcLKLrnW2gDhxK58S4ST/0GZ3cdhCIo0QJPnqB6F6bW1vVkly9Aug5KOIoIR1HDefiHj0UpLH5f/5PSdciuX0XmrVfRhwwnu/YdsiuXoBSWEDhtCqnZfyb6+bvxj59EatE84n/5IwW3vv/47I8blGCIyHkXEznvYqSUWA11PeUBByGzYS37f/ggakERRs0gIlMvJO+iy8lsXIeTTBz6ASnJbN1I5xt/R5omhgCiAcCh5U+/x06mUPLyyT9z8lG3PzuJjwG6+kyO1tgfUxxTN4Z7770XIQTf+c532LhxI7feemuvbgyvv/46p5xyCtFolLVr1/LlL3+ZGTNmcNVVVx127GMhIHeW/AGSh/HZFeKgCu5B1duDHruqcUcLXU0KOJZHiBwr59hg50h195x0E2RFeARY7aoQq8c3hCebBDWAGDQB/D5Pj5rtBBzIZr1Kbf0aqBmGKB+P3P0OqHmIwkpkKN9rGku3eClzrorctRjyamH3OijOR5QOB+EiQuU5ja2ORMldfNi9N+dJF3Ys9i4NszHw5SFTQcgPQ2wPctNqrIxA0zJghBCa6zUbRSO4+9pQfJonnQiEoLgPNO8Fv4vrCM6+95cs3LDrkEVe9+lLefyJJ1DXz4PaMz29nz/sVU17OSFJx4ZEG3S2IDv2Ite8ghPIx127Cq26HyIUxqmaDDvXolQNQamoQZRXg1BIL3wDe+cWNF1iTLkUrV8t9up5WMvm4//Ct5DblmMuewt1zBRYPwdnx0boW4vUoohIPqRiyEQHmCnAa/ZTistR+g9BKav0zPM6mqBll5fy5GQ9qUeuiisdF1zp6TkdB2nbnp2a44VDqD6BGDQBdewFyEAE588/AjJQUovMtGE37EexYij9B2JngqiJXagDRqBefItHeHesRK57EyeQT3r2X1D8OmpQw3X9SNtEptKeQ8WejcjmJmQ2i1IzDt8l16GUVhD/+jU4nUmMUaMwV61CGTwCp76Owl88d9ivceK1WSReeIro1TcRPOs87MZ6snNnIXUfmCZORxtOR5uXKgfePovmI/IKEf4AwnVxYu3IbOb9/f+4LoSjaH2rcPc2YNVtxXfqVJyWfbjxBIHTJ6P4Avgnnom1awexpx4ncsXV+IaOfO+xT6Ib0nFIzH+d1NIFoKoo/gCK39+jAe9gGAMHETzldJSA1xgrpcRpb/O0wfU7yDbsItNQT3Z3Azguet9K1ECw17HUaBSjtAwtL49/1cS0f4S/djDBMUfvLsGJ1KD23LgKKvxHqUEtY/OplXvfdTtN0+TBBx9k0aJFdHR0UFVVxb333svUqVMBmD17Ng8//DD79u2joqKCGTNmcP7553d//oknnmDmzJlkMhkuvPBCHnrooR42s0cLxzQ39oEHHuA//uM/OOOMM8jPz+fBBx9k0KBBLF++nFtuuYWVK1cC3s76xje+gWmalJWVccstt7wr0T1m0MMgDtUYAx5ZshxwzQMG+9I56Ln74avTSk6H202ghUfYFBV0n+exG8xDRMvBF8rd9vaD7vMu2KRzSHCCdCzIHogixsoeVzcGmUpCNo7cthQwEGUVEC1GChfhj0Bqv1fdje2HSh/4/aAJZLoRYbVAoASKRyCEgtz3DiJYAPvWImtPgW2LINwGNRcATneQhHBzDhRd0yEr5SLLB8H+HZ5cxIwj8gJQNBTZ0QChMLqWQvgiOBmB29qBm7bQxxYhggGkmfEcHPw+7ziF8iC5HyVawMO3XMip9z+G4/QMJPnjsy8hlC/w2MM/Qq1bCsXVyJaMV/W0sgc8d0OFiKpxnnwgrxTyShH9huOsfBm1bwXOqneQtoXobEerHYPjOKjDT8XdswN30YtI20QH9KG12CJE+nc/QZv6CfxnnI/buh/z2V/g+8yd6Il2zJVvop9xGUoihmxvRBszwAucGDwEwoW47fsQjRtzxLcVti/C3eUD28FNJA7En6qa973NpaYJRYVAGJGf78kfIkWI/GJEfgn4Qph/+hHKynkQb0U9+3No07+G/eyPYN8WlEgE47bvYP7Pl3Eb9qL2H4STPx7nnbmIxj3oU69CHTTWqyyveBmjTwlWcztuIo06fBjOlg1gCMw1S1CrBntRxm0tyMYNmIteQSkoRQQjKNkMbnMzKApyd713EfkuCF9wOYHTptD204dIznuZwtu/RuhzdyBtG3f/Huw9u3D3NnjHUkqvKq8ZOG0t2HVbPRIsJSiKt398fs/mrWvSjd4veiwL4VoIy0QUFKNKifnOQkRhCdIyyWxYh6YJfKMmovcfiFZWQXLObGQmjX/sKe/7//VfFUJViZw7jfA5F5JetZzkm68jgiHU3rS4UuKmU7Q/+Rgyk0EtLiF05tkYlVVohUWExvfc767jkFq3GifVSxobYO3fT6Z+J4kdO7tfU3QdvajkY12ZezcoZRX0fmnw8YNQlKPm2X0k49q2TUVFBb///e/p06cP8+fP55577mHWrFnous5Xv/pV/vd//5cpU6Ywf/587r77bubMmUNRURFvvfUWv/71r/ntb39LaWkpd9xxBz//+c8PceQ6Ktt2MkHtyOFuXIBs6817UfLuV9jS+8Hq6tZXNe8Wpqof+OHPvS663u8xr47sqrhKmeOqrlextU2w0pDqQKY6INkKmcRBnrvWATsxRclJHHLL1XTwhRChPE/z6o969l1HuQJ9+N3kevZiZidyXz201HnrHS4Hv4MorYFYK/grYPPrMOYclLIJhx3ObVwC+TXI1U8iikcgdy6DaCFK//EQrTwgXVBz07vIOGRng+fE0bASsm2ePrq4BlCQsU5YNQcKC5AdHV7lLmvhmD6MQcXI5naEpiH8Qcgr8rTH+xoh7INUgkdeWcWdv/hbr8u9/rNX8puffA/VCHsXM3ouZU/Vve9DrBm5ayU4NqLvCCgZgBAC5w9fg9pRZF95ESUviO7XUG77P8wXfoNxyfWHNITJWAv24pcQ5QMxVyzAFgH0wSMRe9ajFJZinPdZnAXPYu7YhXHeJ7Ge/hFKQEPJK/QayVwBqoFrBMGykInOAw1/+SWoNSNRBk9EifQmA3pvmG/+DXfh86gFeagX3Yoo6Y89816v6e2K27GXvgT1W5FaAFkyBMw4tO9H2gJpOxhnX46SbsZt3EJ68VLUoHesRXkN9q5tCJ8BwQKkEUERSYi1g6KgDBiHs28X7p4dSFQvMCPWgVpVgxOLE/3+Y6jv0WWfnDObxMvP4x8zEWPIKPR+1YdYjclMGmvbBuztG72qt6ahDRyK1m8g+PzIeCdurM2bOtp67N+D4abi2LE4diqNYmiEL/0M1q6dpP7+PNK28Y0/HSWSh5A24U98DqejjdgfZ6IPqEXGO4l8+t9yTXEn8X5ht+zHTfVSDHEl6TUrMOt3oBWX4h87EXPLRqzdu0DTCIw9hcC4iShHEAl+OLjZ7PG1cDvOUKNRtMiR2zO+X5xIld3nJ/Q9qpXdT65ofN/befnll3PHHXdQXl7OF7/4RRYtWtT93qRJk3jkkUcYN24c9913H3379mXGDE86tWjRIu6//34WLFjwkW/LP+LkWe19QJT1QRQcxkRc0T0CpBm5KODcY67hzLtFa3sVuS6JgWP/w2u2dzs6m+7xWo/JPfBZ2UOW0EXSfGD44JC7AjLnQJCzfTKzyGQGrHaktT0Xm2sfNrb1mME2IRhB9BuCHHI6IpuBuhWQdJHRQkReCcTbwcHT0JaOO6w8RET7gZ0AXz5yz2qoGg6bluIOOgfFzniEyLG644Kla/fenicdhC8PoYCsPRu2zgGzHdrroXQomJ2A9DSo8U4IRNACJqI9ibllD8bAKmjdg9R0RDYFMgqBfG+MSD63nz8Su6g/9z708CGL/t1Tf0MoOr/5+XdQXROyQPaAnlu6NqJqMDJQDk07kCtfQPqjUNIfEmmUyj6wtxEZKMBtXI8+6SKsOc+CoqFNOBul0GtOEnnF6NNuwNmwBDVooPUZCMWlZLZtgK1vo5ZUoJzxSfT078i+8TzGdf+O/dzPcROWR9rCeSgFpahFFSilldCnBuVdSJN0bGQy7k2J2IHnqYQXKdxlIOI6iLxCjDMuwi3vh/XCr2D2rxADx3pODkLDXToLbeq1WA/fhzp1EkpBLZRUYb00E1JpREEZ5uI3kO3NGKUBjJoqspu2oeX5INWJGs3HSWeQ7S0IrR0nWIwSjCASMdztKxAlA72UOdNCVA+Ejnb0fv1QaguIzbiWvB89gWoc3sIrdO4l+MeeSvyvT5J66zWv8cw0UfLyUaP5KAVFGP0Hog8cRGD4OG9Zlom9YxPmykW4nR0HtOjvASUcJVA9GKczhtkeo+N3j+AbMozCB35Ky703YO7ajt63CqEI7H2NaOV9iX7mBjqfeQL/KWfQ/siPiU6/Aa204oiWdxIHoL2LhZpRPRAAu7mJ+OuzsVuaMfpXE5p8Lub2LbQ98SuwLLQ+lYTOPBu9tPywY/UGxefD1/fj5Yt6EoeBOIquCR9g2JaWFurq6qitraW6upqamhreeOMNzj77bObOnYthGAwZMgSArVu3ct5553V/dsiQIbS0tNDe3k5BQe8NoR8VTpLd9wHpZjzy1PubBwij4/Qgj17DGXTpGbstwYRy4LUub1zV8Miq5gMtnCPOXgSwUI0ebgxCKB87qxCZ7sRd8wpy93rYvQUpNAj4QXEQ7fugXx8wG2HYebBjPpTtgrzq3gcLVyD3LIGqybBtthfsUFAMS/6MW9gXofoPJMdpXW4WvRNnKdIQrYD4bhhxJWx4zguNaNkGhgFGAOninSz8YXDTqGELbD/m1p0YfYqQqYSnvY0nIJQPra1geH6+d54/Apm5iRn/77FDlv3bJ59l195WysrKSCQSJBIJ4vE4yWSSoqIiJk4Yx2ljajl17DAG1I4DNQxtDbBnO+rAapyGXUjVQGyYh3LhHRjTrkOmk9gr52O37UcZNBp10FiEoqAOPw2lahiZP/wQTVOI3nAniZeeJvHXPxHx+VCnXovx+uOYrz5H4KZvI3z+wx5L18zg7liPs2M97p56ZDaVO5nmXBZ8PoTPj/D5cpPfq24FfAe+12YamR8lO/tJRLQQffp9WM8/jLJjDcLQkb4gYvt61EvLcEqKkA27kbE4SqQAY+wkGDAOd+XrSFGDtXsX2TVv4T/nQpR9LWAlEYkWqByOFm/HTadA1XBi+7EyDkrAh2YnPK21UDxdezrlNTBt20zk6/+DUlBCbMbnyPvub1DzDn+yVguLyb/prgP7JhHHatiJtWsn9t4GUssW4L72Am4i5+MtJSIYQvEH39cPkLRtFGc5oYuuwjDThO/6JrE/PUbLgzPQJ5yOtX4lSu0w7KY9JF96huhN96CVllNw+/3En/sDxsBBxGc/j2/wcIJnnHPkCz6JI4JWUkbBNZ8HwKzbQeff/oyT6MQ/fDShs87Fbm4iOe81L2WutzsGXVIrIdBKyzGqBnqOHoVFH7vfgpM48WFZFvfffz9XXXUVNTWeXd+VV17J/fffTzabRdd1fvaznxEMeiKTVCpFOHygObzLjjaZTJ4kuycULNOzluoNQgHhAz0AhnpQs9fBzw/S3Ha7LRxwYui2B3NMSCe7K45eRdfySHOXE0Pu8UAlsos0H3Rbvus2vRHMuRmEEJovR6pzj0I7oU6SIhBFPe0zyPGXI9e9imyrRxZVwep5SMNAhOsgUoDQVGQ6hYzVeY1lvSHSH6HqyLwKRNaBkI7UDagoB6Hlbr9nwU2BeaieuRvShR0xGDoRUT4GmtfAyGvgncc9/W6gDNl/DOxZ42mIVRUyDugGqiagsghzdzt6kQ83m0FJxaGgwLNNc20IhqCtkbvvuwsZb+O+X/71kFWYO3fuYffZwbeASkqKOW38SE6vKefmEYUU9anBUVScZBpl746D9nMI/YxLkFLibluD+cJv0M+4FKW0LyIUxX/Lt8k89h1kRxOhc68m2dlO4sXnCF/ioJ59LfqcP5B+/lGUSJ6nRc8kIdGOTB9E1lQVpawSdcAw9DMuRkSL3rfWTEqJXPwXfJOmItUA5sJXkQMn4jauR0klEK4Lhg974V9Rq2qwNm5FGXeKV0nvjMHGN1GnfBZ3wbMEzruMVCZJ9s3XMAZUk169AcNv4DZsxTh/OqyYl+tyFqglPpymfVgdKYzCUqSi4roWSmcHQldx2tpx33qWwFmfQs0vIvb1m8h78BeoZX2PaLuUcATfsNH4ho0+7Ha7rc04HW3va3+52QzJl56lfeZPMYaNIbtqGeHP3orUDDoe/g7Ctslu20T4kk+Tnv8K2eVv4z9lMkLViE6/kczq5ViNu3Azadp/8zP0ftUIfwDFH/Aa53KNWGh6r8tX8wpQPoII3X8FGNUDKfz87UgpyW5YS9tvvepuYOIk8j517buGUEjXxd6/D7N+J4k3XsZub/XeOIHO5ccSgTETCZ125vFejWMCoQjPk/sojX2kcF2Xr371q+i6zn/+538CsHDhQn784x/zu9/9jhEjRrBu3Tq+9KUvMXPmTIYNG3aIBW3X894saD9qnCS77weGAbK325UHWY9J2+s27/Jt7bYgy1mNdT8/jFT6sN+1nGuCOPjvg7x6uyuTuapx17rYac/NwDG9W/VdFmUyt97ghUx0N769TxP7jxqhAkThYAiVo4y7wpMWrH8dt6AI9jbiBsMo/YuR7fVQORYad0L5BM/eav8W6Njt+enaaSjsh6g9BxHbgew7DlrWIIorke1xhKF5mylyx0IoXvNZL5DSAUPA9nVI6SD6ToT9K2HcDbDqUc/iLeKHZBL69oO2FkB4JDYRR1Gi6P0KcJtjCJ8DSgdYFRAugkSjp1OWLsRT3PO1e5Cmyf2/mf2Bdl9zcwsvvjKPF4EfBn2887th9IlGkYkE+A/98RRCoA4agzJwJOasRzEu+zxC0xGKgu/qu7BeexprzlMEp15I8q9/IDn3NYJTHbQzP4my9AUIet85kV+NKB8IBe8deyttCzJxSMdz0cDeczJJT8ZzMBwLymuQW5agjJyK/8obcFubyLzSibNxAVqfIoQRhHXzYeqnURvrcZYtRPnk3SiZFM6L/4vsOxLlzE/jzv8jgatvJ/W9L2Lta0WrHoizZyd6aSHZN1/Gf/5V2ItfQS0pwensRC0tw421IVvqka5X2JW4iEAEmejE9JdjvP0cxlmfQtzzEB0PfpnIfd/DqB3+gY7dIceluBT1A6SL+UeMxdy6gc5HfwpVtaT+/Bso649SXIZwbdzGOrIrFqDXDiU1/xV8o0/prtD7x0xEr66h88nfEDjjHLSyPriZNDI3ue2t2Jn0QfZ3PZHduJa8a76AWlTyobb/XwlCCPwjRuMfMRrpOKSWL6Z15s+9c/s/4qDKrtB1jMoq/GMmYFQNOOppeCdxEl2QUvKNb3yDlpYWZs6cia57F78bN25k4sSJjBo1CoDRo0czevRoFi5cyLBhwxg0aBCbN2/mkks8a8dNmzZRXFx81Ku6cJLsvj8oau+3lo4Husl1jlR3NaQ52dyj2WuR0vOAzVV1u/x4NcMjua48fJX0WEBKiDchO3ZDpBip6mCEoXoEomUnUkhobUG6KxFF5VA9ERY+gVzwhPfjqwcgVOyFSwSiUPcWDL0MmY0hqk9D7lwEeRlEeaU3bg5e86B+eKIvJbJxNeytg8Zduea0IYiOrUjHRWY6wNW8Hyd/0CNowRJQJRBHRMIIU0JhAc7+/QhNQdTtRAyshVjuQsPnQ256A+Wir3HPPdcjHclXHn/5Q+3OWCrLd3/7Vx65aTLu6nVIx8ZJtqOGDj2xCFVFn/oJrDnPYlx4DQBKJB9t9Jk4LXtx69bjHz+R7JpVJOfNITTZQb3wll52lUQmOzwLtFiz10BnZXsuS9O8MAp/Lj2tsI9nzeYPo2iHXnC4mxYiA1HcVa+hTLwMpaiM4LV3kf7ZNuzde9GHDEPKJGx6BwIGSl4F1vM/Rx11LuLyO3Hn/QGxdwdi4qWwbh7+T91M9sXfQ6wZ25SololRXEj6lefQR52CbNyCVlAARhB7x2acTas9zbZrITQFR/EaxszXnsUdPxnfW8+iT/40ef/5M2L/dTfhL9yHb+LkD3XsPiyMQcMpmPEQqZeewR04HGf9CozhE0jNexXVr2M37cV/+jmYm9fR+ftHiH7+zu7GNDWvgPwv3k/y9VlkVi/zvp9dF+e5yqEazUfrU4lW0Q+trA8i92MXOPNcOmb+lPyb7+m22TqJI4dQVUKnnXlEVUrXzGI11GPW7yS16E3cbOZkZfdfAd1SyKM09hHggQceYPv27Tz++OP4/QekbKNGjeLXv/41GzduZNiwYWzYsIEVK1Zw7bXXAp7E4d///d+5/PLLKSkp4ZFHHjlmTlsnye77gPAXgn70y+3vG1J6kgfXzMkd3mNeJNg52YSdhXQ6R44PDqs4TnAF5PX3YplVF/zFnk62byVifROEw14DU2M9Qgkixl7lSQEKKiGQ10OS4e5aglzzEqJfLTgZCJdBohPyqxGFgzxZh9IlZ7AOK2MQrgOFzZ5oZNdWpC+I4ot6ZA3ViyL2FSEjxZ78RFGg71DYsQKCAWjbBxU1CLcRLT+MuasZvSaIu3E9ankVWHFPp52IIzfPRVSdy71fSqJoOl959EXcI2xO6g2vLX4H5daLcRUFx3LRNs6Dib2fXJSCUpSKauwNS9GGnwqAWjsSp34TyqhJyHgrWnA9buFAEnPfINzZgvAFkGa65/4K5kFeCRT2xQmUYDfsxNqyxgvZyKZ7iG8OXFwcztFEeil0hg9pJhGLlyL6D0foPly1AKd9K5mVmxB2CqU4jki3Q0Hcq1QuX4B48+9oIRVtTH+UhX+BQAhlzPkoc/6M1AtQVT/Wvn2ofWoIVGhYe3bgCj90diLCEmFouMk0vlFnYK9eiBAW0vWkP9qYM7DfeQtZMwL/vKdRz55O/vcfI/aNm3HaWgheeHztEtXSPgQv+hSZ+bMJ3vkt4r/6Af6Jk0gvfQu5axvpOS+Rd8OX6Zj5E2K/+iGRq7+AWpxrWBSC8AVX9DqulBI31o69pwFz8zpSb77qeSMLgVpYTN6/fZGOxx+m4Lb7Tjo7HEUohg9fzWB8NYOP96qcxDGEEEdRxnAEZLexsZGnn34awzA466yzul9/6KGHuOKKK7jzzju56667aGlpobCwkNtuu617vilTpnDzzTdz/fXXk8lkmDZtGnfdddfhFvWR4qT12L8YpHQOciDw4oO9v3NRwh+CWH0kcE1IxiDZ5lXSkRAqAplBrl/uSUCGjoV9zZDajzjvXhTtgGWPa5vQsB65ay0UlSJaNsP4zyJSe5C+MljxRxgwyqtqu5ZXGX9PSIhWQ6YT2dYA29fBgHEoAyYi97wD8QYIlSEzftg0F8JhUPxgWoAJyQSU14ATg9Z2XNvCamhHGz0OkexA8bvIvHzv9n88hrjoa7BjHrKpjnU7mlm4rQkl8P/ZO+8wOaoz6//urdBppidnTZQ0ygElJBEkMEGA10RjbGywcVqv7XX4bO+uE+u4OKxzwMYYY8BEkw0iCSUQklDOI2lGYUaTc0+HSvf7o1ozGjTCko2Atec8T0/VdFdX3VtV3f3e9573nEwyAjqRUJCMcJCMcIiAabD7wBHW1newbuNmNm7cSCqVOq71dXd+m/KWXeAqjJpqtPd8+3V7ay29B33BEmRWnt9718F69HeYl38YBnpJ3P4dtPOuIbl2JSIzG5HmbyoFWEnf9ay/G+XaaNEsjMJi9NppyPLxiOzCQZqDz/WNo2J9qIE+X4khnlZlSMT8e9Hz0KonIgMm3t6NeId3Is0A2js/4ass/OzfIBLB6+lDeTpGSTbUTkPYEqUrVOEs7Jeewt67DXJK0SIm4dlzUbEuUlu3odkdJA91IAMaxphKtJCGihbjxl3oOITX04HT00/kc98l8etvIkyJFypGdbfhOS7muZfgbluLLKkkWJqLfsH1eGaA3i/dhDn3XDKu/8RJ3F+nF05jA9aaZTitR5CTZtH/6L3Q34VZO43g3LMInnkuvXf9BuGkCC1YTHDeuX/zsZIbX8FuOkTwjDMZePZxsj70qbdVTcAoRvG34O0QXxxtw6PzKygNjcyZ/3txJGFzxSuH/iHjqNFg9xSg2vdAonfkF486px1blCaOKUwb6f8TbPvP/uOgkt2o2GG/6Km/DVL9Pg1ZC8Le7VBUgiirRh1ogL4uCOWjetoABZpARLMRGdl4DTsRWRFE9jioKEeUzkct/zlkRn3TDXmMzq40XsOJPgaOA167H/AiUB2NUPcq1M5DZOdC8xZUZjGkBGx+ESrGQlsTzHk3bHgUpAuehIIxYA+gmg+jpI7T46FVl6M5STwngSjIRyQsX4938adQG8uqRJwAACAASURBVO6EmG81LELRoXtnUJcLf6CSnkK2I4Wc855PsP7VV4c1/7f/8TFuHBfAaWzBLM1Du/47iEjuic+/ncL6y52Y7/qwT/EAvI5mnE2rMC+8Fq+/h+Tt38Y891KfB943VEQlMrIRheXIokpEOBOVGMBtasBrasCL9fptP+Yci2AEEclERKLpZSYinIEIZSA0DaUUzvZ1uHu3YZx9CTIrB2fpb1EdLYjicahkD17dFvQFi3E2rcNNeJjFUeSsi1BNO5HTL0SWT8VdfS+uE8Sp24zV2kbkxs+hnr8DNylQ8R7spibQdLSMiC+8UFKJqJ6G/dx9OP1Jwp/9LsnbvotQNlRNxT1Uj8zKwzlyGGP+BXj129FyCzALsjDeeRNeKErfVz+GVlJO9N//+xQ/AW887LrtWBtWY3d0EL7yA7R+/ib0UIDA/MVk3/BviHCE2FN/xqrbgVFUQuY1NyL+Rt3X+KrnUbaFXlRKavc2old/4A3uzShG8ebibRXsLjjNwe6af8xgd3SO6VSQW31iHVrlDXdNe+3SdUClXuOqdvy2ynP/LiKB0AO+8sJRBYb0Umin58NxOiCCOYhgDsq1IHLA58QefBVRmOcrUrS3QHEZYubFqG1PI8I5iIoahG76VIVoCQQykOaf8XasgUAOwqnw3dcKJ/j0g4pZCDcJTtwvaLPj/vFGgvKgLw6xZt8OOKcENX4u7FqDmjQX4bqIRC9Ky/Bl4gx9qEBQD4JKO9QpATi+pahjY+QZOAkLHBctJ4Lqj+EOxJF2EnloA6JsNurwZp/PWjkNMawQ0i80VMKA7lbobsdwW3jHjOrjgt0Vm3fxoTMWI2QrynLwnvkF4vyPILNLRz7/RgBj/hKcVU9gLLoCAJlfgsgpwNm7BX38DMyL34d7aA/a2KmQUwbxGGqgD6+/H9WxBXZuTl/LMFpZDfqsc5GZI7hL/bV7QQiMaWeiT5yF9dJTkIxjXHAT3tpH/M9QsBRlrcPr7EKvrUU0HMDuTSBfXYGQEm/FfRgXfRRZMQ2hXFTrAcLjJxN/6Hb0AMj8MjSnD8ZNRDUfwEk5yHAQ0d2M6uxAaAKhC6wn/ojIK0K1HUYmehHBDPSaiaAU9prn0c9YgNfRTEoIvId+TWDJe8n5n9vp/e7n6fnWv5P9tZ+dct/fSBi1U0m9ugojP5/4Uw+R89mv0/PDr+Ac3Evf3beS9aFPk3nZNVgTp9H/yJ/o+c33ybzqBvSyylM+VvicC4gtfQSvvw+9qJSBZU8TOf+S09CrUYxiFKM4OYwGu6cCJ+lzXEfCoE5uAKTxN2dn/56crlLKL1Cz0gFcqh/6W8GO43kjF54JzRweHBvBv7MVfyeCmQipD7Utp9YvEGvfB31tUFYNzQdRSRvRfxBRuyBdWHY012lB7CDEPKieh7BTqF3rwAamSZiwGNbcBUd2vIY7+jpQHhRU+tJzpoRkKyIrxw9492+GomJwkwgjgsobA71dvjbr/pdh/HzY/jzoOgx0QyQDQknEQD8g0N0EXkYEr6cHUVSGzMzAqz8Eax9CXHEzonkLiiBq39Yh1Qz/7PiXKU1BEeFMVEYmixdM45bbHxzW/BUbd8CHL/Qlsywg4SCe+hnMugw5ceRCKllcgXtoD27DTrRqX11An70Y64k70Eqr0SfNQlkpVG+3n5XNKUCMqUFEMsEMvuGzE8IwCCy+HK+vG+uFRxD5Rcj61ejXfxO14wXcfXWIsnzIzEEf6EC79BqUiuA++0dSf/wOqmwqmumizVyMt+k5MmZNJ9k5gLVtLVo4jDl9Io7jYvY04kYycdraMQszcdP8OPdAHebCC7DbDiOSvWjF43C7OtDKKkF5OJvXoE+ciXIs7F4X7y/3EDz7QrK+/CP6f/Vtuv/zQ0S/87u/6rZ2OmFUjsPet5Pg3LNxO9swps3D3rGeyMVX03P7T8l457sxx04g55P/Qe89v6H/kbt9tzXDGDbjJMJhtLxCtKIy9OIyZMbxzlUZS66k7893YdZOwT6wj+TmdQRnznuzuzyKUfzj4W1QoPZ/EaPB7inAq1vp2/GOhGNtfL10sY04OmUrgLSMzKChRPpf5JA9sNRAHLMu9WMeml+o8zfBRBxvqYZCoRzXD8KclrSSg81bWaSmQkFEKILIroCcKt8HXAhU5QLY+RdfbqxxPzTsgknTEcVnIvTjjQ2Ua0PPXkR+FV5lG6q1FQ7tQhbPRpzzkVNrk+egVv4WyidArBPyx0HfAUR2Nmq/7ev4DjSjNAkl42Db81AxHloOQpUBZgTcAejrhHAWhAMQ6wUkIhJGywrjxWOoznYwDbRJtai2LryHvgm1i5CpFkThxHRrjvmiEwJyxyJC2dDdBG0NLJw4EV3TcNyhGYjG9i7291pUZURQMoTSDLz2Lrzn70asfwY5913I6mmIwHDpIn3uBVhP3oEsKkeEMxFCYF54LdYz92Fe/mGMGQtO6Ty+EZDRHILvuhH30D6sdUvR6tYhx8/G270Vr78PUTQB1XoQ9+k/oV14BeYHvo67+wXUq6vwUibu/m2AjtKDBMeX4KRaiR/sxnn+aYIXvgtvr0RrP4g2YRJOazvKskEIlOui5efjSB1hJRAFpWiFxbitTYiMbPR8B2f3FrSq8cjiMTiH6kisfJ5AVwsZn/gKAw/cRu+n342IZqFXjsc4Yz7GrLPRzJHl7k4HzLmLsPfugL4u3PZWsj/wMdpu3k7vbT8k61NfJbF2JVbdDsLnX0bORz5HYv1q7MZDvuyYY/s7UQo10IF16CBe7AXfBMMaSgCIjEzy/+t7CMMg86r303v3bwgvPI/Uzi0kt/gzDlpWNkbVOIyqcWjZJ6bTjGIUoxjFG4XRYPcUIDKivkLASFDuMY8hO9fhgePRdYlKO6Ep0sEvgFCgbBBpZQQHfE1cf/r6+EykeM1+jz53NCCSDDOzeD1ogCZ8LeG3CgpEbzeqswviFqr7oO8Sl1cD0UrQDcRAOyorF/p7UNKErl2IwjOO25XQDD+bXTHXL1LLSqLq9+DlvYw2/tRkoYTUUXOvg/X3QcVE6KiD0jnQucPn/ybj/jVyHSABrge65j+6DsGYybA/7RXuOGldZMN32kv0Q+UUpKVQsU7clIk60oIIRZB5uSjp4jb2I46sRRSUIIrKfKWBowOr+uUoOw6BKJTPJXPSIuad+RNefvnlYX1YsaeJsRMzsdq60cbNQOQXIoJZqH3rUNuex9m2AjlxIdq0IfkeIQTmO67FfvEhzMs+6D8XykCfNh9n/QsY8y44ucuqFKqr1eftdrWCnUK5J6ADvRa2hbFwCTJvuH2qVjEObdGVWM/ei/GRb6PVfQZPy0OWl+HlluCt/jPq0bvR3xlGyy5Avec/EI/8FHv/JvSzr8Ld+iJ6RTVaeS2ZxUncnGuI/+EHiLJxeAMSse4VtMpqVNJBC+ooy0VmF0IghHIGQIAxcwGibitabgHW7q1oqQTuwX1gpQgsuIjUskdRqRTBvi4iV32QjPd8DPvQflKvLCP5/GPE779t8KMrTBO9ajzmgnegT593WjLAwjSR2bm4hxvIfPdN9P7hF0SvuoHYY/fQ95vvE5hzFub4yfTe8TOi132Y0NyzCc09tWP0P3I3Hf/zn+R/5XsITSfr+o/R8/ufkXHp1Ril5QC4PV3YB/YTX74Ut7dnZD3ZNwue//2qF5UQmDITfUzVKRufjGIUbyZGE7t/G0aD3VNAqjGJc6Tt79qHOsq59Jw0V9c5hrfrHMPHfB0MFrTpw9eFTO877bx29BjqdQKLQfthOVQk95bRGBTCDGCUFaAnOhEtPajccgh2Q+d+KJoMzdugdj6sfwoa62GMwuvYfvyuPBvMDESqC1UxH5F8DhXIR21+Ds/VIXhqEnKitBZVuwgaXoGiMdC8AYrOgII2aDkA2ZmIVAzMKCqY6Tuz5RZBSz2MX+Dr+roJ6D4CuYUQDkN/n3+dOjugsAARzsPoa8Ttd3Fb27DjDgErgf7OryKUQh3Zj1u3Cbe5Ea+3C4IZiEgUzCDK60e8sh48i7Nz4OXXtH/F1r18+IxzEcEE1p5taDl5CHcfIhhFDbQh8yvwdq+EzChyTO3g/STCGcjqKTg71qFPScuRjZ2K27ALr60RWegXMSjXQcVjkFZT8Noa8brb0ydPIHMLkaXVGOOngxk4aUkq5dhYj92Oecn7EeGMYa+ZZywisew+vMZ6hGGgMgpRdesxr78FJysT96UnsB/4EVrlZMS8OHLJR9Gf+CX2mqXInDyUNBDBCPR1YM4ah3HLXSR/9Bnk4ovxDuzAObQPqz9BMJCB0AXJ/XWIjCxUdwyjqIDEY/cQvOQavNZGTM/FrRqPWvYoblsTyZVPE7z4WqwXHiYeixFK3oZ+/nsxKsZiVIw9rp9uaxOJlc+QeOxuvD/+bCgIzshEyy1AlpSjl4/FmDAVraDkZG/b4xBY8A7ij/wRr6ud8OJLSNXvIXjmYqxNa7C2rMWq20Huf91C3123Ej7vEszaKae0/8wr34/b3kLXT79N7me+htA0sj/4SXrv+S0I6atzpLO6wZmnGEmfJiilcFubSe3cwsCLS0EpZCiMPEHWWWZGCUydhRY9dQ76KEYxircGo2oMpwDVd9jnw44EqaWr+s2h6v6jS/nGWfKqo5q6Rw0kBk0kUr6xhJt6XS6qENLnFesBPzN6VDDe8yXJhDqBZe6bAQVurAu7vh77SAegIdx+jADoBdnI8z8Gr/4eymehti6HVBIx9x0jFw0q1zeZMKLIgul4r9wKtoanbETpHGToFH6oPBdv9xrkwqtRB9ZBohsyI6AslJuA3dsRJWNAVxDKQbX0Q/9hRNkkVMNmmHQudLZA0w7/1BaM8bP3rS0+hSWcBWMngSUh0YHIKEK17cRt6cBLKNAkKlgIykWYOlpOFiIShngfGFkQzvfdx9Kycc+vXcelX/7RsC6UFeRw4OefQXguqnYqXlc3TiKCOrAForlovQ2ISAQRzEQ/6yJEKOTrDx89Bfu2ICsn+25xKJTrYr+8cjArJ6QGwSAilLaVzclDRLNGuO9H0tg9+vxInxEPQmXYy57EvPzDgzJnR+FsfgFr+RMYpbkoW6FiXRhLPooon4z32C24BZWoHZtR3a1o85ZA+wGIZGH3OmiJdvSxtSjPH2Rp51yHlxzA+vWXUHk1qM4DeEcOkuwcwMgMgBHBmDgDt24TMq+A8P/7OfEHbydw9kVgJXB2bkROmEn8199CCc23ZD7zPLyGnXgdbYTOnI/MzPDPiRCIrELIL0PklSICxxswuK6L27AHZ/8u3MP1eO0teD2dKOsEdQMAqRTBi68ifPn7T7hJ7K5fIKPZhC9/P30P3EH43AtJ7dlF792/QUqFSsTJ+uzNqO4OnJamEa4TJxysGGMnEJw1n+6ffBMRiZL9sc8Py5S6vd3YDfuwD+7D7en2n3yLM7syM4vA5OmYYycOmmN48QG8/pGVd9yeblLbNvgUDikxaycTnDYLGRm1Sf5HxdtJjeGxc6pOqxrD5asOjKox/LNDNe9EJbtP9Kr/kyDTWVKZtt9Nc3Y9qQ2+dnzce4yUFMdYAAvtmHV5/P+vDQ40Adrx/NVhrVSer2Wbiqezyo4//Z5eVyelO3v6IJVL4MwLCDoJVKoHLxbDOtJPbMWjBN0/YVYUQPseqJgEu9ejkhrauEXD9qE8F9XegIrtRASyfZWFkhlwYD0yVIhq24BHMK2OgL9UcMKMtudC+SS8tY8hpp8P9S+BI0GCUB4qEkFlFCCSbf5gJKcAWupAdyEzB1r2QkENtAX9QUmsG6JRv2jNU5BKgMiGjBgMBFDdDVA2G01uQtOCqL44wkuihAbCgVQv2DG/vbH9iK49EMxCjj8XUVrLOXOWYHztJ9ju0LVsau9mX9cA48uKESmJZvWhFUUQF30dteVp7L5xOGsexRgTwHl1HcYVn0QaQ9JTImsi9srHMC8ZkpHS3nP6ObtKedC6Cf2MM7CW/gnzshuGBdDatEUYW57H7nEwVCceIbydK9GKaxAl49GyC2BhFs6BQ3ibnkeOmYBwbPSQxOlRaC4IXNAMVGcTMq8M8+Pfxbnzv3FyKqD5EHrYRNkeOHG04lLcui3Q14nQDcLv+RiJR+7EmDYXY9bZWK+8QMbXfsnA9z+PspJYK5eiT52DUVxOYu0a363QDCIzo8jMw2h5B9FMgTA0hKYjSsYiKiYhAmE0TUMbN/mU7Idd16X38++FYIjwxVePuI05eSap9atQnkvmldfT+/ufEb7ocgq/8wvab/kyCOj98c0E5y8mcsG7kNk5yKycQTtadXRwPAIGnn2c1PZNZH/8C/Tc+n16//grsm785OA107Jy0GbOfdtkdcEPwK1d2+i7//cox0HoOsbYCWi5I1seazl5RK+5AUjPPOzdReyph/HisfTM2GvgeQhNI3T2OzCrxp3OroxiFKM4AUaD3VOAnHDx676uXAfspP+wEoPryk4irKQf1DhH5a1emy1J52OV60tM6Yb/0DQ/iJWkHwqEm6blivS709kWxNC6FgA95Ksr6KH0emhQ6eDtCs9OwOFVKCcBJXPQcsOEchoxS99D8rFHsLqnEalwYMbF0LANdi7DixRC0w5UzxE/cBcCAmHIMiCq/Cxr6UxUxx7wBKJkdtpQwmXQbll5gDtyUls5qOZ1MP0a1O41iNJaaN8JuWP8wDMahZ4OCCh/0GD41sUq1o+I5qKa6hFVs1DRIug44LusZWRDZib09PhtObwTUVGJGlMB9b2InsMoPQCehZy6EFFSm1bbSFNUHN8URBlhv0Do4FbU7qV4254gqGUyp6qINfubh3VjxZ4jjJ84BaHpqJxqaG9APfMzuOATmHWrsOYvwX5lKUapwn76Dox/+fhQBX4kihwzDmfPRvQJs073bTAIISQUz0b2HEAWN2GveBRz8ZAzmdB0ZF4pWmYpzsuPo42vxPMEYvX9sPDd8OwvkAsuxSifjdPfjtfcgIxmo02Zj2tJrO0bMadMB9vC27YcbfH1yHAW+vVfhfu+Q8oBTddwU47/uRSkZ2ockk/fR2DJewhd9UGSTz+IVlZJYNE7ST33EJlf+yWxn3wFr7sTe8MqmLmA4GXvQxg6XusRvO4OvJ4OrMP14Dj+UNkMIoMbkFIhc3LQ8vLRaqYiiyohu/CkqB+appH1g7vp/X/vQwaCBBdfdtw2xswFJNeuwN6+AXP6PLI+8jliT9yPCAYp+v5v6Lr1R1ib1pDavJbUlnVp6pUaRq8SQgzZjRsmaP7slYxkYNSMR8svJHrdR+h/9E/0/ek2ou/76NtWP1zLyiE0/1xC830zDWXbWPv34PV0jbi9VbcDt70FrbCE8DkXEJg0ncCk6a97DC+ZIL7qOQae/wt6cSmRxUuQGaOZ4FGcOkY5u38b3t6Rz/8xCE0HLQOCw7mFp3r/KM9NB8zpwNn2dVqVnQ6Y7STY1jFxmUr/TWeIhUSZwcEfIfR0wIxCMXLmVkjDn/bXgm+p8pgIFyBqLsKzYnB4td/e/EnISA6hxWfhdjr0r3yJkBtBzy+F9mbUzqchp9CXABuIQTIG9gC0OyhzNxTUIo2QH2y2NEDhRchIzim1y8tcj3r1Hlj8adi5EiWjiN42P9MajkDDASjMQ2gpMAxURg50d6GKSnyr2+5GyCuGrkZ/wKMEBMOguv0imWQXquZGxJ4nUXlj/OK84qmo5k1weDOqs+mY4sehCyRc2w/sS8fB1HMRyX7Yt5bzplUeH+zuqOcji5uR592I6utAbXVQ8U548n9RExdh5DpQOw27fheGa+OsfARj0VWD79enLSD15B1olRN944s3ESK7Cn1aLvbq+7FffR5jzlBxnDjzcrSV96Ki2aj2Zgh04U06F7H2MUQkD89SCLcJ/eKPYN/zDTzbhvYjmCVZJBocvJSNBpBdiGrejygZi4zmoV/9BewffAo34aBlhnAHktiHDiBCEZRI4OzcgOrvIXjVhwldei3J5X/BHogRuPR9pB6/i8invknyoduwt2/A3rgamV/kB3yODaEMZCgTbeqZvryX8vBaG/E6W/F6urAbj2AfboZX16Mcy/+B0w0wDIRh+vxlcfz0vzBMgtd/lqzv30XvF98PZpDgwncM30bT0CtqsLasxZw+D6FpZF7xPlK7ttB3xy/I/eAnSM6eT/fvf47UjUFNZzyfUiM0HWGaCE2CY/t20QlfrcHtakVpGuqJB8h6/8cJLTyP1PZN9P3pNiIXvBOtsORtG/QehTAMAhOn/tXtnJYjDDz7BF5fN8bYCYTmLxrMfr8WMhgatF+2jxym/8kHUAMxAjPnEZzpX4NRjGIUpw+jwe7bEEJqvsNXYHgR1cn+RCjP9QO+ZAyS/ahkzA8CU/GR7XGPBsf6gD+1/lZBAX1tKOEgsqqg5mJIdKBaNoEUiJwitNghMq54N8nlT2HljCeUpSOjlSgH3AGB1SnwOmKogR40r4dQQR4qkUBZfb4DmufCq3fjGeE03STNrTYjvtawOMGPTuVsRE0n6vmfw6VfQOxdjzqwCYIWIjcHZWj+vjwHJVyYsBC2vQApC6LZ0LgLMedKVNNe6G+H/jSVwTTB8Xz+8Y7lUD0fOuugHVTnfgjnoWLtEB8q9vIfaYUNx/LPTWcTHNrq01xyylg0vZLvPvrKsC4s37gTpV2Nu+pe5KSzkWdfh2o/iLf+Edi3GkpnYNZOIBXrxentQd+xAievBH3qEF3BXHQl9opHMC++/nTcAcOglAIrOSiJJgJRjPM+hPXkr3C26+hTFwMgCyrwrCRaZQX2lm0Ys2chotm4W3chszKQm59DzL0AojmI8kmo1gbUgZ2w+N0ELswn9fi9BM9ZBLEevN0voZX4BWQyrxSZFcWzOhGaAE1gb9+MOW02omU3oqQSt7GexL2/JHjlTQQXX0Zq3QpSa14gcMWHSD1+J8F3fQCtvIbk0/djvfjEYAaUYMiXL5Oar7TS2+UHweEoMisfvbAMGfD50VgpvFgfqrcL1d+DF4/5U+ZHoetpB7ooaqCPgf/5FKFP/w9Zt9xJ73/cgAiECMxeOOzcBs+6kNgdP8aL9Q3q5AYmzcAor6bvvtsJLTyf0t88gEomUck4XjKBSibwEgm8xABuRztOyxGUPWTEIoMhrJ0bcA/tRc/Np/euW8n+6OdxWxoRkSySG1/BaWs5enXRsvMwqt9i+TFNQy8e8zcFm3pxKdF334BSCru+jv6H7/Fn914Lz0PZNmb1OEJnnY9RWk7WdR9GuS7JzevpvfOXiFCE8KKLBhUrRjGKE+M0pnbfykzXacZosHsK8Jq2+pmwE+CvZSzEa22Dh6kppPm4Uj/mf234+mvfewJrYSG1tJ6rX4T1125f5bl+IJzsP3EB3psBpVDNeyAURUWS0PgyIpgNFYuhfikEs6AgD9HWRmhWLU6wltjShxHhFoQm0QtzMUuKkBNqEYZBatmLxF/eSnieQAXDiPyJqL59iHP+FSENX4s3FUMl+yHRA8m+kZUrXAfW/RHmfwiR7EM99yvkRZ+EcBbeitshMwyZGSgzA+H0+8VHTh9KatDbCfkl0NWGirVBbqkf7KZiIPMgOxva2kAKVNs+5Lj54FioCWfCtmWI4lpUMO5PnRtB3/0tnO0vhQQzE1rrUR37wAggSiYCkgWTKzE0OYy329zVy77syYzXe1FH6lA7VyGqZyIv+Qze5idh+4uouVcRmDWP1JpVuEJDvXiPP8goqwFAZGYjC8tx921FG/f6U7dDl1Wh+rrwjjSgutv8AivbGnngdSyOBvZSYpx3DUIIhNQx/+XTWH/+CSISQav2uZ9i3Gw4sBlj8lTszdvQYg7GZTfhLH8Ae18d+qx3onXuRl94Fc5D30Pkl+GtW4qsnY5WPYnUzl0EqsoQFZPxDm5DVk4DQCsuxW7vRDguUkhcz8ZFImxBoLcBd9x0nLptJB+9g8AFVxOY52vZJh6/h9DlN5J66l7MOYvQyscSv+unfhZfMwAFHUewWw5h2/ZgdwlGICcf0dgAiQQqMTDyudGNoR88KwW9jWBbCCuBrKwm8auvEbzu02R953f0fvnDiMBXMafOHny7zMlH5uSTWrOM0IVXDD2fESXrw58l/uxjpHZsIjBxGjI7FxnNQRaXva4slxeP0+s6eO0tpHZsIrRgMX333kbm9R8n/vi9qEQcLRRELy1HrxiLCEVwDjdg1e99/fvgdMKxGXjhKZ9XawYwJ0zx+xzJ+OvvTUMIgTl2AubYCa+7nVVfR9+Dd4LjEJp3NubkGYRmzyc0ez5erJ+BFc8Qe+phjMpqwudceMIs8ShGMYpTx2iwewpQjXuht/kErwo/uDmqazsYvB79X/oFRhL8yqbBvYJKF7eJo8VpcvBHftCEQg43o0CkM1/pY/7DaENmhFEDXbBlF6LmTMgohdZNEMwGqw+ha1A7B1X3HEbgMOZnvgeaeVw1u1KKQKyb1KtbiG/YRthWiIKpoIVQHTshqxqMiG81HM4BKl63WV5OJay/G2ZcDqmn8VbdhTznBkTZeNSePVBViehshcwgwvVAdSOmnI/a9SIk4pCRCQe3wPTLoHGnn8VTmu+6B34QBH6W9bwPIXY8gioeh+ppQk44H1U0wdfs7WyA1sOoRJ/PD9ZNX5mhoApCBaiDm0G4hEMmZ44vZfXuxmH9WL7sBSZ86WbUlqdgzmXQUo9a9SdEUQ2qfBq8+ihq3uUEZs8l+fJqvOwCnD//L+KD30JG/QycNvMcrCd+j6yoRZjDCyJVPIbX1YrXegjV2TLI8xRZeciSKrQx48AMgGH6g7KTgNu4H/svd2IsuR6h++6E5hWfInXfLYPBrpxxAe6ulxD5mZhF2aiwjvXyUlABKKrBvvdHqDnnoC+4CsZMQjXuRBSPw+sbwJwxHeuVlaR21SHbuzGqaxAVU30O6rhZyO3bUa6H0CSarnCbDiFTNuKcd6OtfABxxjlYQR6hywAAIABJREFUG1YiXn4GY/JsjIkzkTn5xO+/jdDlH8B++Rn06olkffcPKMfGa2/GPbQPe/cWn7rQ1+MH/7ruD7b6ulH93UjDRBhpN0HDREQykHnFyIJitPwiZGklwgwMO1dedwexn9+Mlp9H8uHfYpz9TrK+eSs9X/s4mV/6Hmb1UEAWmLeI5LIn4JhgF/zgLXLxFTgdrThNh7Dr9+L1deP19x1TwDryEFpIkJNm4W5+icS6VYTPOp/40kfIuMKfCVCOg3PkEM7hepwjh33dafkWZpM8hRY0Mapr0avG47Q0EfvLQ3jxAb9eYiQ4DkZNLaE5C08pKDZrajFralG2TWL9anpu/xlaVg7h85ag5xeSedk1AFgH9tP/yJ+GjDyOxQjiSTIjil5QhMzK/scmXb4O9IJi9OKyt7oZbw6O1u+crn3/g2I02D0FiJIqVOYJ9FmV5wcsnnvcQ3guflDrgXtUASDNtE27pikh/Pe7TlroPL2/o8VTR9df+1o6AFaaia/RlS4Y0QN+AGyGIJCBCGZAIM0nDmYgtNMjXfJGQCkP1dsM+17C2/MilIxDBAxfuzacB/EjEKlEDTSAEUAax2dAhBCI3CoCFS1Y/RYDm3YQyV+FzKvxTRgS7dDXgHeS6hMiezyqdCbsfh5RPgu192W8Vx9GVM4A+lEHD6NMHREpBNdGoRBltbD9BRjog/xSaNwNCV8fl3gP9Bzx1RrCEUgmfSmx/Chiy3M+baKvCdWyD6/rIDLZB3oQskogvxqh+9dadRyArsOIni5UZyMYDKpMLJpefVywu2LlKv715kyYfTlq4xMwaTGy5gxU425UIMvPGm95HjV+FoEzZpFc9wqifAL2HV/B+Nj3kSHfSc1YdCXWo7chsvOHn6d0ZlKOqUZMn3/iQZhyRp7yPf7Mo40ZiwhnYD15B+bF1/sOe7qBNnYS9tZVGNPP8YPurALo70IuvBpv5d0YZ09BTjwf98BurHtvwdqyFUwdfdZFOM37wY4hpIZqasB83+ex7/4+WDGs3XvRvfswL3wvcuKZCP1u3KPmEkqhZ4ZJtbdirX0ec+6liPV/IXDuO7FefAwRzcFtbcI891Ii136U+J9/T2DxZbj7tuM2H0IfPxVZVIZWUoF55vlDp8N18dqasPfvxN25EbftCKq/C3+gm/5+6GzFaagD2y9SVJ7yB8SaRGgGIpyBiGYT+JcPYD3zINJQ2Ouex+toJeOz32LgV9/F/MGdg8fUJ5+BWvoQzqH96CNo/+r5Rej5RSdxjYbgHDlM38N3o8ZNx9u3jfjLywnNXUhywxqCsxf4SgcVNRgVNae039MJZdvYB/aSWrsCr68HAZhjypE5eSNuL3MLwXXpf+J+VHwArbCE0IJF6HmFJ3U8YRiEF55HeOF5uN2dDCx/BrezDXPcJMILF2NWjcWsOv56jNh2pfD6+3DbW04olfbPgJM2qRnFPy1OSWd369atHD58mPPOO49wOEwsFsM0Tcy30nUrjbeDDt6JoDzX51Y6yWM0cdO6uI6/rlxrxPcOU1gQEvTA4ENpJr71mfCzfI6FSCVQiX6fq5vWzvX1d9NLxwJUunAtkA6Q/aWQ0p9iHaHw5c2CyC2D4lq/r4BybdSulaj2PYiKsb4aRawXJl2FeuH7UDkNbeK7/Cx3ohtibah4B8S6UMWT4PDL0N6OlVTY+w4R+cjXEd11ww+qlB88mpER+66UB3Y3ongOaueLoCnQBKrpACK/Cjp2onKyUFs3Q9kYZDQTFYoizHyIe6jmnf5livf6hXRaBOo3AwrKJoLbBy1NfqGZmQGRPOS0C1HdO1CRUtjwGESyRlaK0DSomYPILIbWej/4yatEbXmIZRv2ctFX7xq2eXFulMYV9yFrZqOMDNjyLKLqDER+JapxN17THtj/ChTXIAyF5xokX3oJo6oc0dGEdt2XkDlFvp5zqh+UfRwdQRy1vX4joFyUm0REq8H2sJ75E8Z5VyOz81F2gtSDPyZw7RcRuoHXVIf35E/Qbvge3rrHUPvWId/7LWQ4C7dhE87zd+EOeIQ++Bmc5U+iDm9BTl6MUgJh9SLPu4HULz6HVjkWZSm8QC7GWUuwb/siTk8CEdDxUjZKj6DNOpfkyy8SnDQZc8pM1M6XEAuvJfn4nYSuvAln10YCl74XEQqTeOJe9KrxaCXleEcO4LY2gnUM1zUnH1lahVZW5bvjHdt920b1d+P1daN6u/xlfw/KtlADA6iBPlQq6c8qOQ5eRytKQeCcJVjrVyIG2hDFVcjsQlKvribru79DyxjSmI4/9QDuoX1k3PQFxBv0Pe40N9J7z29xOttRXS0QySY07yw/i4tvymBUvn3tgpVSfvDYN7LMpNN0COfAXoxxkwmeuQi3o43E2hW43V0jZ4M9D2GYRM6/FL249ITHtPftIv7yckARnDX/hLq9MjMLLTd/tKjtTcTbIb442oYnzquhNHyadHbjNv/yYv3bMo76e3FSwW5zczOf+MQnOHjwIKlUimeeeYby8nK++c1vopTi5ptvfjPa+rp4M25Gb8vj0Nfy1zc8JYi0IYXuB5qa4U9t6+mlZvrZ2nQmb9AGWMBRCgSeg3BS4CRR3l/Jlmmmz/t1Pd+u1kkHwXbKNyVQHm8dSV1Bf4c/rVlQhayZh0jzjt1Vf4CcLIRIQSgXssahOvZD0ybIq4RYDyQH/Gy6EGCkuc/F43yJsrJK7AOtWHUNZHzmB8hjZJx8SS8LrAFGjCg9B9Wyzc/AFs2GLU9C3hjo3INq60KETIgavsh8UyuiNB9RMhnV14qYfAXq6V/4QXpOAbQfhEmLYcsysONQOBZkCtqOQCTXv55ZReABZ70Xsf85VMlMhFL+YGTYx1Xh9bX69IhYJ4QyoGYWwrZQ+9aQSKTIe98PsZzhWY+dzzxIrexG1Ez2rZf3bYCsAkRhJepwHbQdhtYGmDwPWur8gHd3E2Z5IaKzCW3SdET1NCicANJEuJbP9bYSfp+shM8pN0N+0Z8RHlqX+qB8mnIscHylEZzkyLeE1KBgPKL/ICgPFS7DfvY+jDnnI0uqcLY+idejMM/9Fz9Iue3TiPlXIactxn34u5BMoV3zX4hAGOfBb2Ad6UWfPhVtzHTclQ8ggkFk9SxUWwPKiCLGz8J54H/RLnov0gxg7a1HNLyE3dKF0CSe7aE8yPzG70kue4zk2uXIjCih2XMRh3cizv8AiXt/RfhDX8RetwzjjIXoY6eQXPUMXtsR9LGT0EorfGUGqaE8D9XTgdvkG1goK30edAOtpAJZWIaI5iAimSesCVDxGG5bE15rI27DHqztG1GajnHGWTgNexDNe6F0HCplgZBkfel7Q7f2QD/x+34Luk7osuvQTjGTeyI4zY103fpDvM42ZEYEEQhj1EzwZd8Ki3z+c2c7bm/XWzq4PupYqRUUEZg0A728+qQpYVbdDpLrViKjWYQXX4qMZp9wWy/Wz8CLT+O0HiE4dRbBuWedMFhVlkVyy3pUaoTPhFJ4/b24XR1D2cz0fSGDoX9aGoNZO4XgjDmnbf+jwe7/fZxU+uUb3/gGU6ZM4cEHH2TevHmDzy9ZsoSvfOUrp61xbzfIGe96w/epPNcvlHLSUmODGeChrC9WAhK9Qxla1/LpDoOWwN7InmdST9MWohDKhnAOhKI+z9O1fUkzJ5VeJhGO9brua28GVDQDYbuonna81XdDNB9ZMR2mXgAb/wyVtX72pGUzcvIVeK3bobcFQhGIlvnBccDn0andqyE6BvJaoLkRo6oSooX0//Bz/nT7STfKRSsuJ7hgDqJjG0w4D/avgTFzofcpn17gpBDhiO+sZtuohj1QlAOt2xCFNahEB/T3AAL6G33eqpP0pchyiyGa4ytmhCJ+f/KrYNNSGD8N0dmAyCoHO+XLP/mNAkCG82HhDQgjhNu6F3Y85xfcuQ6hgM6ZEypYtaNhWHeWv7SKCddfBR0tiMMNiDOWoBq3gaWhzXgX3q7VKMuC+j1w7nVo257GDCVwmlvRMzLxOnuRegO4yp93OGpUohyU0P17U0pIdSM8Z7gusOuAq/wpeMvyA7BUCpVM+ufNtcF2hvroOYisHIyr/xOEB731GIsvwlmzGs1KoZVPxj34Al5/DzIzG6qmo7a9gJh+PnLKuXg7VuA+fSvapZ9ATjwL3V6JvXEL2oRp/ixC03a84n60uZf4cmw9rYjqKTjP3IN+9iVo5TV4B19FCOF7vEmB8hT2xhWEr/4I0jCwtq5jYPVKAuPHo6+4l/DHv0z8N98l9P5P4zUfJtWwh8D5l6OSCdzmw9h7tuKtbh3KiBsmWkkF+syzkAXFCClRVgq3+SBu80HU7s2oeP+QyZwvyI0IZyKLypBFY9AqxqFXTYAz34Fc/gTJZY/jbHwJOXEmKhCChs2QUYDbPtzuXEYyCV5wOckVT5F87hGMKbMxp//9hg96yRhy//ULdPzg67itRwieuRhpGLhdHdj1e3B7ulDJhO9YZrx1M4MCgVZWgZ4Rxdq/h/iq5/yZnmAILWtkeUKZk09w5jzM2imYtVNwuzuIP/84XiKOGEnNxlPoZRVkXPQuMAOktm+k9w++i13k/EvR8oabVwjTJDT3rFPqh1Jq5OD4nwQjnvd/VAhOo9Du6dnt2wEndYds2LCBBx98EMMYPpooLS2ltbX1tDTsnwVCav60rxGEN7j41rMSMOBP6RPvhpbdkOrzNXtHCmql5mvtvoV3vCiZBNFCRNtulJ0L8STe7tVgBkFp/pTzQAfIgG/1Ofu9YPUdtx/l2ZCVD3tXQE4peG0QCGIEuwj+x89PqU1KKaxn76T//vsJXXQZ5pggFI2HeI+vKep6iHgCcnLB0BCBICrpoKwgtO+F6UvghTsABzLzoP2wX1B2aLsfwIbyQLrQ1+PbTbtJ6DwC2UWIWBIVzUM53f7gRTs266Qg1QO7d6ACecgxUxGLP4bnuaiHb4ZwiEVzJhwX7K6sa+Nfq+ZCbzOq6zBq/f0ox4GW3Xg5pchJZ+NZCdTO5bDhWcSST6GfY+H+9DOoUC5uYz1gIdv2+cYXHC2eTBdUGgE/qB3ox+3rgXjc3/+gbJrws5RS84uyDANhBHwJLSMIYW3wFhSOg9fTQerXn0fkjkFffA0iFEA7YzLOS8sxLroefcoE7FVPELj0A8iz3oP3hy/gdbcgauYgehpRXZ24z/wOecGHkDtXoeUX4WzcgF5Zi9fdDG17/YA9GEA/+zq06eeQ+vUXcNY8h77oCjwVQOgCzwGpCYRSWK8sI/COawi+60bQNMTODdjtndiJfoKP/YrQJ79J4tZvEbr0WrSqCSQfvI3Akmsxxk7CGDtp+P1lWbhHDmLXbcN76dm0gQO+CkJxGdr4McjsPD+7e0zW0Rvo97O5+7Zjr23zB72OTeAdV4IZJPXcw3i7NiLHTUHVzIDdryJzi4k/dvcwO2G9chzhqz9E4pE7cQ7U4TY1ELz4mr+76FUvGUPeF79B+82fJ7lmGTISQebkoReXY8xfhFFU4k/Vv4XZSKU8nMMHSbz0Im5HGwifqxyYOQ8RHdlSXCXi9N1/OyhFcNYCzEnTybjqhtc9jn1gL7FH7wbXJTB7IVk3/TtebzcDL/wFt7uL0Oz5/jH/xnMhhECMqjeMYhQnxEkFu7quE48fL0l18OBBcnJOTZx/FG8epBkCsxxyTk670XNSfgb5rUruKg/q10DDK1AyBVE1G9FVj5cRht3rYdoF0LQBCoogHEU1b0SOmQeR4uN2JQAv2Y3atQoyiiC3Gw7vg6IKvM56ZN7JF8gIITAvuhG9uJj4uhWktuYQWXIZMhVHaEFUIuUzSzwXFTBRQkNkBWAghRJ9yP5GVGYBaF564JGAsrBPS3Ft6G/zC8sqx8OBvX6hlZ0Ez8M7sBF55rsRGSf+nKlEN6p7P+rASj+7UzjFp8UIyaKZNXz7zuHbv/js06hkHFkwFlEwFiYsRvV14K19CLXqd6jqeYgpi1Cxbti3Fm/Nn5ELrib40W8R/+23MGfMw929GXHZjcj8MahUEu9gHap+C15H2jQDgQgEEHkliKnj0fKK/ezLUXk9TU8vjUEpvZF+6JWVQGx9Fs2OoUqn4Kx8GBIxRNlYtOoxOCsfQ58+HpFTgNu4H23MWLyicrzHf4L27i8jgllQHoWWVrxlf4TSCciW/dh1u5GTJkC0ANr3wfzLUeuewes4jMwvx/zXH2Df+jmclY9D8SRkrBm3N+UPNpTC6xrKkAYvez9Iidi6FjlxBvFXXyZ45zeIfOq/id/2A8xZZxK84oNYK/+CSh79HhXI3AJkcTmyuBy9ajx61fihfiuF6unEbWv2s8G7tqBifQz/cI5wvuIJEg/9jtA1H0EYJsln/4xXtxVRPclnwITCJJc9PizYBT/DG37fv5F85iGU7RC/91ZCV9xwSmoDI8EoKSf/y/9D+ze/iFY4BhEI4na14xzaT9JN1zK8tZNJiIxMtLJqAuPPxaiZ4OvfrlvtD9RGgIr14aWSGJXjSO3ZTmL9amQwRHDOwhPybPXyajKrxqNsm9TGl+m/+1fIrFwi5y1BZueR3LCGntt/ipZXSOQdl6GdINAexShGHdT+NpxUsLtkyRJ+/OMf85Of/GTwufr6em655RYuvfTS09a4txu8ujXQM1Ime7irFTDEwx3k4+qDP/JC6mkbYCP9nDYUAByz3dA+/PXT7Twk9QBknlxF8WnDGVeikjHU7mWw7QlUtBBZPAmvpR46DkEiAYFs36bX6oAx8064K5FVjYrs9Hm9oTyw2iCaj6pbjssq/5JJw8+magZIAzFSVttzILMAbcYSIlLgdDYSe/h+zKmzCWhB0PvAlf52ZgDiFugCckNw0MGrfxUx/XxY84hPXdBN6G/xl3YSeluheKyf0Sssha4OyMjxtVPzavDWP4KIvLaQJx0hODaiqAZRNRNROgcv2QuH16b7I5lfU4Cpa8N4u61dvexe+gATx9UgZlzgO2JF85HTL8bb/gLK9WDPckR+IaqnAupfxetuRJx3E6FLriWx4lkCZy3BefQ3oBu+s1dOIbJ6KvqF70NG8xBC+RxwK+FrOB/l9DqWzxd37GHfrCe8txN9iNxiVG8norMe8/xLILMKe91S3K3rIZKP2xVHrx2LvXIZsqwG7ez34j73e9yltyLPvhZxaD3MvRRW3AuhbDTloSbOwFm3GbMsH6+3Be+VR5HnvB9v6W8R130dGcpAn3MB1gsPo01fgFu/3qeWqvTDSWHXbcWo9bWGg5e8jxRgb3mF8AWXkXxpGfb3P0P4cz8g+cDv8Ho6CV41ZJmrPA/V3Y7bfAh7zXOooyYRUvPVGsbUIAvLMHLyRzorJ4SyLAbu+SXJh39P8KqbCBkGiWcewtu7DWUE4cgBhBnG2rcbc9zEYe8VUhK65Fqsreuwt28k8eSfEEcpBlIiM6K+znI0GxHN8bPN4chf/V4yq8ZR8psHsBv2kdq+kdTeXbgDSZSyfMfGt1QZRiFtB1W/B7dhD4kVT4PU0IrHILNGVmMQ+QUYpRW4/b1YOzbjxQfQS8uJr3xuZP6x8sBxkLl5RBZfQvDMRX5hW08XydXP4XZ3YIydRPaN/4bb3UXsqYdQ8TgYI5wXpUAItOxctIIi9IJitIJiZGb0be9MN4o3CPKoFOlp2vc/KE6qQC2ZTPL1r3+dpUuXYts2GRkZxGIxLrzwQn74/9l77zC5qjPd97d2qNzVOXerlXNEOREkASIn2zIYmwm2rwds30mesWfOjO3xmbE99tzxeIBjcxljYzAgAzZBIkgCBIoooBy61eqcc3fFndb5Y5UCqEUyAhn0Pk89VV21a9faa+29+1vfer/3/fGPPzFqDHKwTfFbh4PIZKw0Q1VGn1jazfDrkCCkm5EWO/HsZLi3J167qsDsTe9ltj9ReHZitE4/J4cbQSEysmMhhD+sKv39IVUkFAgrabLz/OYo+1uR9TsgEFYuYs3HYewciLcgwn4IFUDZArTAWZYbpcRrfhWObIGKyRDrgrSLmLgIESpG6kqjVLgpZS/sJBn2cpAeNNcgKucgCqrw3ngaqQuszl6sXTsJTS1F94Ug6kP6/NDRjoiGIVyAHLkC+eyPEJetgr1bIBSAvg4lo5ZfCS3H1Tk1ci4k2yEnHxpqVBBuGCoYyC9HZBWeUZyGlJksckaJQWiIMbMR+ZW4L/xQmYToOsu+8ySv7jrwpkO65+7/5i9uuwW5dz1i1ExExkRB1u7CO7YV7eI7FNXl+Bbk0a3KCa64CIpG4OzeiSMiBK/K6LN6doZLnjlXHUs9nz4O/QO4HR14g7EM7/TN/SxdF9LWaf0vTz4J28EoL0MP6ojcArSqKYjiqTgHt+Ad24HX3IS5/HIwyvFi/RgzFuO9cj8y7agscNlIRHYu5E/A2/gw5Jcg+9uwWwYwl10BezYjhKMmeqYJ6OjLPw+6Qep7t6JffgfW7+9XagKGgbRspKEhssvxX/sFfDMXnDyO9NqHsQ/sxJw8A6uuGrvmEOH/559wdrwKqUGMqrHo5SPRiioQBWVqovCWfvA6W3Cb6/A6mjPBzbCn97CQ6TR61VisfbvQdAjc9Ge4TcdIPf1rnLZWhHAQVZPxutrI/f4vzroft6uN1AtPKltiUFJ6EkWVcB2E6Uf4AqcK6jLjpReV4V9yxbtvr+e9yYXtQ4dtk6o+RPL113CaGpSjnM9UXPOz3R9dV7F2fH70ytEI04c32I+07bMEuy5eLIaeX4SWlYUwTAIz5+GfMVfxs6XErj1CescmAALzlmKMnnD2gkTPwxvow+lsx+1qx+nMyI6dy5TfeY7AlJkELlrwzhu+T5xPBWrPXj6GstC5iblaExbXrqv9WBaovSfpsebmZmpra4nH40ycOJHRo88frcQPJdiNdw9fNS7JaOBmglLPectrFbT+ocVf4qTrmnGaA5uhAqOTVe8hhG6eyqpZCUjHVXYtnUCeeG2nOXvk/NGpMYj8Shg9+1QGTEpoPYBs2Ins6YCJl8KxVxFjpqp1CUegjb8KAC81BH3HVEbUikHpLBWcHHhRSX8ZYehphsvuREOCkwA7iRzONe0t7cJzkcf2oM26CeEP423/DTKvHOKdODX7SFV3EpxUijFmJLKvW2XEAkEIl+JJDfa+BvOvRxzYqDR2hUBUjEA2NkJ6SC2nj5oNPUcguwQO71T8XjsJheMRBVWZYsLUaZMbCbqpAi/XhtJJMNAFvS3IoUYV3Js+vruhme/99JdvOqLL5k/j6cfuJxTNgZ5uaK2HeTcghKO0jTvqYdYKNDOM1ELIV3+j/skbQF4Z6a2voVdWYRTmqjorTxVuSVeqS6GvD6+3V7nBuTZaKICeG0ULBRl2dqZpyjjhxEqGUOe3TMXAb+AOxbEb2hHJQczFKzDHT0UUTcbe/jgyCbJmK8alt+Ec3odv5W3I+p3IpiPgz1IGEuOno025AveZ/4aqqYjmA7iFk3FqDuErjyLb2xBjJ0DPAOQUIJIJtGW3Y/30TmQoH7epHumkkJiZQjuBXpCHJyL4Lr0B/6IVJw8l/dKT2DtfxRgzES+dwNq1BX3hVfgmTMetr0H2dirJsEQM4fOhZWejF1dgTJ6NVj7mD5aUsndvwmk+jtvZiebTCdz4pzgN1cTv+4EqsJOaEv34jwfRfe+O56nuJ2mklUKmU7htTac0fw0dY9QEjLFTsA/tBtfBv2DZO+/0PIT0POyG4yR3bcXpaB12G6+vB6e9FS0cRDdNNckzM+oZwyR+pJS4aQvZ34OXTICmY5ZWgG5gFJcQuuxq9IySg7TSpHZswq6vGb7oSnIyu4uuo+cVohcUoxcUZdQgPpnBrvD7zzBY+SBx3gW74XMU7MYvBLvnPc6Hk/FcQsqMKcXpAXQmG4xnnSb7lFDFWWfuAKH7VFB8WmCMGVIOZOdJRkA2HkA27kdMW47IPkWp8PavRXbXgeVCNB9CPoRuq4K6E5qlug+yyiBvjHJcO/AYYtoqvKZXofp1KKhQwf9gAlEyHnS/ojC8k5OXlMiszM2l8RjavFUgBN7m/4FINmKgHc/IUzw/zyMwrhyjpBzhxSGQB1NuwXv5pwh/Hgz0gpeZMAVNGMos87s2YsxCpDMAMmOecXSXarOTgpJJKtB10mqc7bTKpmYVqW0tVdkuBZBbAfufh9QAhIJs7DRZ/qXvnHFYVcX5/OSeu7nhikVqItd2DDF2ETKQh9z4MKKwEm3GStUFySGVFfUFIBQFf5T0pheRwlSmBoaBkI5S9fBstOxstcydXQA5xWhZ+UoZRNchGctoQceUXNwJVQIpT00aZeb8tpN4sR7EuEUIrx/P07BeXo8XKsQYPQnf0qvw9r6E29mF8GIIfz6up+O7/FPIjQ+gzbwWd/9GaNqHtvAacP14tbsgGIJ4L3ZXEm3qHLSmvdDdhHbz3yLX/RJx0UpoPITXfhynqREi2bhtTUhbIISHa7kYM5cgmw+DEcaYfyWB5Tec7Ftr5ytY659EHzUGEcrCeWMjTrAErXQkRkkFRsVI9LIRCM/FbW3AOX4Et7kWOdSvpLAKijGmz8eYfBEiGHnP16fb3kR6/ZO4sQR6KIj/xj8h9r078Ww1EdcqxyL8AaJf/+572u9wkLaFU1+DU3MQb2hAFaFVjcWc8O7spP8YIaUktXcXg0/+Gq+vBy03HzMne3jeuefidnWoiU1JJViWMoIY6EPLK8QoKELz+QjMW4pv0vR3PdbScXB7u/C6O3B7OpUT3ycU5qjx+CbPPGf7Px/iiwvB7h+Gswa73/rWt971Tr7//e9/YA16vzgfTsbzHfKEHmomKD4ZHJ/F0OKjgNAMZNFkOLpVZT+nLVeZ6tQQcusvkUMxmPdp2Ps7RMVIyC1FhEvP2I/0XOhvUyYNAT9y33oIBlXGMBLNmB5IlKAtnPJJPAtnt70Lpi+BwU5EUqJNuwLpWngb71V2w1YS4gPI3AiJXUeRQ3EABdONAAAgAElEQVTCi6chwlHIHYO04si6g2CEEek4xHsVP6qgENo7VOA3+3qEdJGdByFaAK2NKhgsm6CaGggr6bhQtjouw4T63dB5THG780aCMJSEXPsRwIagn1Qwh4JrvkXKGmYSBFxz5Qp+eu/PGWUkkIkBtImLkZ6L9/S/I5bchlZQpfp0oBNvx9OKY1k4AnpbFZc8UwxHXhmiaBRkFympLumpQD4ZywS2MRXIBrNU4BvMAn/4bav+pefirr8PehoQ065A2P3ISAgO7sbJrsBpjaGXZqPnlCNrXkVf8BncbWvx+nvRq6rQwiH0pZ/DefxfEZ6FuPFbeI9/HzFislKiKJ2Ks2835rLFeJs3IOwkYs7ViL4OyCrAO7oNp/og+syLsV57Hs8TaBp4jousmoGRk4N3fJ9a0p5+MYHrbj8ZrNjVe0k/cR/6qPFo+SVQvR0pBJ7t4loOXtJBmkFEtACRW3DKPlkTyGRcWQkP9KoJ7Vuz4Zo27LkqkPiXrsQ3azFaJIvUM7/G6WjHyC+AQJD0pnUIIXGFibBt8u5+/Kx9/35g1x7GOX4E2d+Lf8kV6KXvrjj2jx3J3dsYenr18PJfnocbG8IoKETTBcQG0fIKkDn5kEzidrUjbRuzciQiGMIsryJ06ZVooT+sOPACPjicD/HFiTasuWLsOQ12r3nx2McyjvoEidNdgNB9EPQpzd3T3/+I2jMcpJVAtO1FRoMQLkFuWQ2jZiEqJiMjhWDZcPx1MCNI4UcMdiNDZUpSLdkP1pDSIXZSEMqD7maYfCNUTYLjeyE7H2wbETjhVJWRzMrYLg/fGxJZYEH1bsTUi5ENb+A1H0SrmKIoJLoJsg+COQgvTWjJbJxjtcRfryG8eBJisAUx+Wbob0AOKRMBYQjFm5aesoLVdDjyCoxeADmV0FMHM5bBpsdVYFyUqdS3bejvBK1LtdsMIkYvQOaUwNFNEO9GBqMqSA6FQGgEpMWX/+Rz/PS+Xw7b52teWM+GyZP41j/8I99YNplAXzsitwRxxV3Itf+Jd/030Xx+RHYR2vTleEe3IbqbYM71iHC2yjD3tiF7mpBHNqtM+Ok/oCu9ZxHMUo596Uw2e0AVe77t2pIQaMu/hHzxbuTBDYiRMxEDMeTYqRjN1fhu+xvsfa+SfuN1dN3EfeUxAl/4Z2RfB/az9+O21eIRRLv8y8jf/RDeeBoxYgoynUDklEP/cUQkgrVjH77ltyDXPIB3aDv6yAkIf1BxhgVKLcLQEWkHNANNlzDUhmWl8Y+einP8EGLvKySH+gneeidC0zHHz0C74xskf/nvCE3HWHoL+MPI3g70njZkaggx1AupXiU7F85GGz0Lfeoi0DS8eOzsfFbHHpYN4g71k1x9H07NAUTpKKTw4cRjpKsP4ZsxByFdPKEhPBsRzSG5/imCK244c0fvE+aYSbh1R9FnLiC17vcEb/oCWtbHX1kgeNECgu/AGU3u3Mzgbx/Es1xMdGRDDULTMApLIBhBDvRj1dVgN9VjN9Ypzv4ZkAif/xTVRUq0cBZ6YfEnmsZgFBVjlHy8grML+GBxgcbwHiCHmsGJn5N9X0AG/lwIFmayqQeQiV5Iu+BIGD0L+fqvIeXB3Btg/xpESbnieJpBMCOISDGEi8AfQR5eDVmjM9nEIHL/K5Cbh9D9ECpUAabnqgIrz0ZZ3w5foCZdW2XDtWzEhIXIAy+hTbsW79irYMUQyV4omAYtr0NxEbK/H2tQw206TmjhLCieCd01SH8QuXuLoiQEQoiQXxU+JVPqvQWrEB21SJFUmdCqubDtCeWgd4LKAqcVwkjIKYKCMarYJbtEZZl3PQXRHBAumCbe8r/ju5++mB89vwfrpGnDmVi4YAFr/uEL5Fz9JYRu4DUdQO58CnHNXyspO0DW78PrakDE+1TRo24i8sogv0LZJPdnXAYDEQhEkLqpstmpmDJOObFMe/py7dmWblNxZMsRxMJP4f3+e0g9gFY0AoJZyPrdiGVfQkSK8XoO4jQNkn7mf9ALywn+2T+g+QM4r/4a58A+RaewBtB0C/3Gv8dbcw+ifDwMdSGLxuOlXNy63ZgTZyBrd0MiiXHH/8Z99Pu4TdXIsXOh+nXs7iFlq+s5iOIxkB4irWfjL8jDbaxF92uQW0Hoz/7upO6p19dF4mffwVywAoEHtnWSdymy8xE5hXi9HXgttYhEL8K1VOFh6Tj0CbMR0TxEVs4pZYR3gJeMk/jVf6FnBfFfewdadh7pLesYevBu9IJCRCquaOi+MCRj5P3k0Xe133cLKSWJh+8lsPJTJNc8SvjWv/jArIg/DnCHBhh89AFS+3cjotkYPh0ZH1KyZXlFaP4gTkuD4km/FVIirTSc/pnPhxYIoYVCn9gCNf/kWURW3njO9n9eZXavPMeZ3RcuZHY/8ZC+qHLLOu8gMhJa5qml0D9WJLuQ3fsQZgRKpyKEAd3VyJotCG0u+CJIa0gVmrkeMncUWtkccNOQ6IJ4F7K3T2Ub9SDIOAx2QNHlyn2t6ShMWKiCR03PGCHo6qGbnKIznAbpIeq3IwNR6GtHNu1DTFyKt+cpVTB38DnV/0NdYGc4p0YQX65DqjNI6lA1AT0I46+FvQ8jpsxB1ldDy3GkLEDkZSsusq7DjieQl30FUb8VKYYgPYhY8hmklcjYRptvWvaXA91QdxCObVNKEPmVEC48leCxLNAE+mAT3/7sMj67eBZ/81IDL7744rDdv3XbNlZ+N81zwSzyVtyOVjkVT/chn/0x3tLPoxWORIycjkgOKlqF6Yf+DmRXg7JCNvyQVwZCQ/a2qAA3nXz/pZlWEgoqkXteQLvxn/BWfwuvP6hk8lwP2XMYLasEzR/FN7kSo+CLpF/eSuw7XwRfAL2oCKMgF+2ilbhbn8PrPILW9AZk5SOlROSPQFjdyKSBMX0R9v5dmNkRpC+K89gPFV826EP2diqnr4yDmhCamoj5svB7g1h9BmZxOW5fN3qyj9gPvk7ozu+gF5Wj5RYS+voPSP7qx+p4QEkQFpahmyGEMQDxAbRIDkSykSmlCqJZMbyda1V210q9OcA5Gxwb/Yo7CH/xGyQe+/9Jr3kQc9YS/IsuJ/ncalypIeIxZS2bioHn4TTXYVSMer8jdAaEEARv/DzJpx4icM1nSTzxC0KrvvwHm1R8XKBnZZP7pb9ESkn6jW0MPvUYri0x0BGtDbiAFo7CWSYIwvQhQhFEMJSpxdBVcWg6+YkNdrWs4fWNL+ACTuCsmd3ly5fz+OOPk5uby7Jly96WNL9hw4Zz1sB3iw9j5pVacx9eR8swn5zm46lpaokp8xC6lnGKyjil6Vrm/dM+P/Fa08A48fq0bd/xBiYzwdpp+r26LxMAvzn4VRq/mc9032kas+q986ZQzRqCoQaVAIuOQDbsgJ5eGDUDuff3QAjGzIW2vYhRMxC6H0/zQ087orsemRyE3BxEyIS8iRDvg3AEeWATFBQgskpUoHsiW+qm32Y93YPsMmg5iAznQ3MNTFiEMHzIrjboPY6IFGQsnT3INiBUjGw7BnnlJF7djG/sKMwFN0PdJmReKex7HemmIB5XzfAFlAxVYgiKRiHyqpBOP/Q0IpZ9De0t1JOT/SQlxDrwuo5C/UHobVc0jnRcFSBKGyIRROU0ZCyBd2wXsmwuT27Zw9/+/GFa2od3QJwzdRIv/Prn5M1cqnqgrx256SElUzZ5mcoit1ar7G1O8TmbZEnpITc+BAVVSsO4cgryqX9BBgoQXgpGjkW/6HNI6SL7qhFmNGMxXIVTfwTr1Wdxq/cghYGWlYMuYpjlpYiLb0Vu/T2idAzEexHTL8bevhmtqhh7yzb8i+bh7d4CRhAG2nG0LES8B7d/AMcCLeBD8weQugnCh8DGzq5CT/UjdA0tvxj38B6MJVcRuOrWM64rL5XEPXYQt/YAbkudonXoBiKvFBGOIKSDTAxCKpHRgzbUsrZmKI70WRQbpJVE621CX3Eb+oS5JJ9+SPl4BP14mCS3rMepq8UoLkLYFl5WASKdIPcHD3zgY2fXHsatq8YYOwlr12Z8s5cgIlG0rCj4AufNveZ8gNvXw9Azj5Hatxt8fjRTH56QIDOa1IKMVXyGZ3Pi+vuE9mlg+lwiN93+zhu+T5xXmd2rxlF+jjK7LXGLa56r+WRldr/61a8SDocB+NrXvvahNeh8hm/B9cjk29MYpOuqghLXybzOyI65zpvel45630uf2MZVS+VO8i3fdd9Zsiyjf6nUGey3KDZ4b9kw8yxERt5Je7M+o8joA38UkFJJ+JSPxZy+GK14itImHmpU1fNaNyKQo7KGjoMWjOIN9CIP71DTDV8AisbArGsRoTzk8z9GVlYi3CQku6BwNFSOhsYOZKxbBYKupQ7XME+6jp0JD9I1iMoZiPodyKpJcOg15NwbIbFfZfs1HXCUakLXPgimQYTBHiI0bwax13ag5byGPvFSqHsJgkGEkQN2I9IfgO5OZMkYRLYJ7bXIScsR1a8gcwqRG+/DK52UMWSwwbHVuQKIvEoYvxR99CXIkYuRPbXI2p1KW9hKgd+nlj5j7YgJVyOO70YLanzmm99j5XXX8b0f/pifPvk8jvtm+bWdBw5z+ao7eHH9BvIrR6HlliCXfh5vx1PIoW60qSugdFzmnEvipVIw0KZkz2K9QMYS2BcEf1A9+4IqE3zCPMXwK96yljFXGRYC5t8E23+nCt96W2DBrbBDOX2J7g68ZB9aMFdJlQXyoOMNRHYVxsiJGCMn4uxai7trI17OCJy9mxFuPeZgs6Kn2GlEVjGy7Si+K27FWnM/5tWfIv3bn+NbfgPetucRviAM9iMNP3rIwE2k0XxBjLwcnJ4ehE9HigBm73HcMXORzTWI7la0qfNx928lXr2fwPWfxxg9+eRRaYEg2tQ5mFPnnDr9UwmcfVtxju7FjQ0oikikWNHJPVdl8YIh8PsRgSBaTt4ZckveUB9uIgYbfoMcHCB04xdIrf89rp1GzwkiUkkC85eQ3LoRIzcXkU6ghaP0/fVtRL99D3r2B+eIaY6ZhHP8KHge/oXLcVobkQ01yNggMp0+cdR8tDzTU/KLWn4helEZWnG5UhL5EDPRem4+OV+4E+m6JHdtJbn1ZfCGu+9LvFQSLx5HJlMIfzBTf/B2NQcff3j+0EfdhAs4z3GBs/se4NYfQcY+HvIu0nVVEZeTftNjWNmyD61RIPu7cdMeXl8PGGG0gkr0kgr08iw0JwG9A1BQgqzZDNEKxKLb0M7yT8nrPI7c8ziivAIKp0FfC4TD4FqIQB4YAaQRBOEDqaF0u4aB6yB3PAYVFYjCccpoIbdMWfsWV4EIQLoPkR5Uv1O/GcrKkOERcHgzlJSBnWBo4wGy/vQb0Piy4hU31MNgG2QXI61+GLLUOIRDCM+DJX+COPYS0tDAn30qSNSVDq0QKKmq/naVYS0ZhRYtQUZKkY9/GxAQyQKfAZEI2tK/xNv0ALKtFukaaMvvQCsdz/p167j++htInQxATmHW2EpefOk58nNVkZEc7EEe3AzZeSq768mThioiuxQKRiKyi9Qyv+dm9J3jkI5BOq5MCFxLFVi5ltLhlW6GizxM/6djiIpxYESRrccRdgoxcTHegReQDXsROWWImYvQKpcg3bSaGLkuBPIQYSVdJ+003vbfghEleWA/1O7GN30G+qR5yIbDiMqJ0FmHNu8aPEvgbFmNXjIW641N6PYQWlY2Xk87MloKPfVY3Um0YADfHf+IXP8rnO4uRCAL6QvCYBcsvhnnjS3oMgX5FWh2DM/V0MtHIEw/Wn4RWnElekklIhge/lJwHdzjB/Gaj6vlaQDPQ6ZT6u9UAhkbUpPoDITpQ+TmY0xfgLt7Iwy0oE1ciG/FZ0m99gJe9S7Iysc5dhi3uRbHAT0YQJ+5BD0SIf3SM4RXfZnApR+cK+YJ/m7wpjv+YOvhcwnpuXi93XidrbgdLer+czZkDFP0ytGYk2ZmCsM+fEjXVZq/3R243R2fcOmxcfgnzThn+z+vMrtXn+PM7tpPWGb3bFi/fj27du3CdV1mzpzJypUrzxpsfNwgBzqR3cMLjX884Edo506Y+x0hQZQVYQRBhsNIV8M7fhCv5RD2rg7886ajZxVB3igQr0G8DxHvhawzLVVlaghRUIWMlCOT8YxL2hCYZRDQVVzrWUq1wLXVj8uzcCKlhDEzoeUQBLJgxFxo3Akl5UoZIbtC0Q8ME3rqwQgpG99ABGmEwXMR/giRJVMYeugespYtVBlOa0gVkcX60MIBZI6BDE+AYzuQuoao3Qa+bEQ0RzmovbWzpFQZ5Ui2yt62HMNrOAgF5SpDL1GBseuqoignjSgciZi2Evnag8hNj+JGi1g+4SKeeeIhrr/ldpJvCXjfONbE5cuv4cVXt1JYUoqIVCBDJci962D2NWjhTKX9YA/0NCOP7UTa6VNrEZquuL2BLMV/Dedn6CKn5tjibebbEpBNexDTLkN01SJzRsC+9TDzakTDHrBSyPQgUnqKyuI5kDMWWrdDqBAhhHL7iuYje7vxlZSTbq3HrTmENnYyxPphsBvKJ+Ed3oi++AvIqil4yTi+klzSBzows3IQuoDsImRPI7pfw3M9nE3Poc+9HmPH0zhdnUpurXgUbFyN/8a/IPnK85gddbjRIozcCF5bM6KwDPr78YaGcI68kdEUlohAEGPiRWjlo1SbdQNj3AwYN/w/8OGKl7zYIM7BnVgvrMa85Hpk7T68w1tJD/Xju/5LxLetJ3jFZ7B2bUErKsVMpbDa2jB1MMpH4i66nOQzvyG97SWyv/njs47Je8EJ/m5i9X2YE6YrGkMkighHEZHou7Ib/jAgND1jzlCMOXnWO24vPQ+3uY70lg3I2ABomnJT8w9f02GMnogWiX6wbdZ19IIi9IIiYNoHuu8LuICPG95TsPvDH/6Qw4cPc/nll+M4Dvfffz+bN2/mX//1X89V+84rGDMu/qib8LGHjA/g1R3Eq1dOaCIrD3P+PPSeVlIvrSNwyeXoRzcj8kYgezvwarejjVsIvc3Ivja1rC4EmAHwHLRZ1+Nt+yV0H4GSOdB9DApHArriKBtBpBFSHOazNspDUIPMqUJ2NSIqIojiyUpdIR1X5g3SBV+ukj/LqYBUGww1wchJcHQnsqIUzWcSnjeJoS2HCU3KRY9ElbFFfy8yMgLi3YhUD7JkFPS3Iw9tRq74Ilr9dmRno2qLEKfkzjRTUVGyiyDkR1SGIJgHPW0qlMzQyHFssA1kZ7WiPQx2wOVfg5d/DrqHHExwqS/GM088ynW3rCKZfrPc1Z6aBi5dMIcHH/gfZl+2EpFTDDMuR77+FDIYUT8ULUDkVyAqJik6yYmucx01oUjFlH1xKn5qufVdqDGIVBzpSKjdjZhxLfLVB5FjpsHutWpuYicR8SQy3oaIlCMi5ZBog2glDDZCttIIFqPmIgeeQx85Fl8km/RTDyD2vI5ZNQ4Z60ebtBRZuwOvvxX9opV4a+5G5oxE6EcR6QToGl44W9Hy/TrOgI1/3ETc+sMwZiGG2I7T0aYCtzkrcB//KaEbvkzi9W2Yg23YqSSBxcuUY19fF95AP97ggBq/UBYiGMV9ZQ1IBxHNxRg9CWP8jLMGT8MtsevRHPSFK9ByC7FefBRjxkKE6Yfmg1iP/n8Y0+bh7HwFc9wU7I5mZGc7ZuVIUq88j/AFyLruVuJI3LYmer/2abL/10/Qi8vPfl28S2iRKOFb/wKvrxsvNog30IdsbcSLDSITH7G6jfTUNWIYGCPHYYyd/K6CUqFpGCPGYIwYo3bjurjNdcNLxUlJ6qVnwLbwL1rxidEevoBzhHNpC30eTDzPFd422H3++edZuXLlyb83bNjAc889h54pjliyZAmrVq36xAS7F3DuIcLZ6FMXKa1RwGurxt2zDtnXg+/S5aQ3rcc3+xL08XOhezUkh5CtRxF5FVAyATHYiexuhIEOpOlDxHoQBeOQdjMi1g7SRujZEM4HMsGYmwQ7rqx5h+NHSw98WYgCiazrR7ZXIyqnK/mywR71HX9EZVE1T9n91jdAZAhRNBZpmJBIQDQHXUuRdeWlxH73KL7ps/BZCQgEYKgPgYsMhxB6LqQHkYYOr/waOe8mtIJKIGPUYKfAOtFeF9J90N6ueK+lGoSD4PdDKg1WWk0AALqrEbNvQ7YdQbQfQlzxdbyX74PmXYhL/pxLN/+OZ595huuuu47EWwLeQw2tzFtxDV//3M38yz2/ICunGLHsT955PHUDwtnq8T6hmX68hjcQPU1oS27He+VXMGEmtB5WVJyBGAw2QaQc4YviDTVCzkRo3QbRSoTQEFkFEIxAfwsinsSYchF29T60omK0+ACyZjtiyWeRWx5Du/qvMBZfg/3yGhXkppJohg6xPhAaQtPQgkHSr72Af8nlyKSFkzcWHYnb3oY4tgf9pq/g/P7n+C9ahhXLQe+ux3rlWQjnQjQPEc5Hy69Cy8tH8+vI/i7kQC+eJZCWjbVzE6kXnlB9mOHoEggiTjz0s9y6UwmMWUvwrbwN65Wn0EsrYMQ0RON+RHYB1pH9BFd9Bfu+HyIMHb1yJMJOk9y8AfvoPrL/7kdYe7eT3rmJgf/9/6rVijPGVEcrLiew4kb8sxa+qzEUgSB6aSXnq1bMCRe49GsvIOND6s2zFF2KUBhzwnT0qjEnCzOFrmNUjT3r/s1xU5DpJOnN60m9shbfjHkYk2aeF1ntC7iATwLelrN75513kkwm+ed//mdGjRrF3//932OaJldccQWO47B69WoAfvazn31oDT4bzgdOzQWcG0gp8fY/jXO0GlGUR3rXIfyjR2JEXaTnQ2SXqQ01HZFbhsyvBCGQ+14E04eYdS1y6/9Afi4ia4Sy2tXfg4Sc9CDZCwUjkak+qD4ChRFE+TTkoQ3gi4KZDdaQoktERkCsDXwO5JQhYykly5UbRWgSfLlIfw7Jbduhq4XAjPGI7i6YdjGieS8ylAuuCUMtMDSAzB2B8IffrBYhMzSG5CBEChRn1/Qpcw1rUBXk9fSozKHfB6EgFJaiL7xTfb2jBgbakKPmIzc9qLK982+DI9t5zcrl2htuJJ46k8MLMKI4n7vvuZfrbvnMWccrlUqRSCTOeKRSKSzLwrIsbNt+07Prunieh+u6J197nse4ceO4akQIvfsY2mVfhFQM743nkH0NiIE2KJqIGD8BUT4f4Ysg7QTEW9SYpHoR+RNVu7ob8N54HiZcglt/FPvFhxV9YPRItIJy9CWfxXv1V4gZVyJKxuE1vob1yC/A8zDysvFEAK+nE+HEIaccJw1ycAD/ZddAMAvr0H70ZDteZysiuxBjxWexH78bikfilU7E62pFeGlEfEBNWGRmsiVBRHLQSkchkGhODOH3oeUVIvJLlRRZPIZMxPESMWQ8rr5nmAjDB8EwelkVeuUYtNIR2K8+i1Y6AowAzs6XQZMInw9Zvxc3awT+S64h/eITOO0tyPgAxtT5yL4u7PZWcB2id/0TenE5iecezzi1vRmulYLuTrz2RmQyoYwNcvPxLboc/6XXoH/MNXW9oQHso/txG4+B5yHCWZgTpqFXjTtl9vA2kJ6Hve917MN7MSpH4Zt/6bvWUL6AjwbnQ3xxog1rrx1/Tjm7Vz9b/bGMo942s3vvvfeyceNG7rrrLpYtW8Y3v/lNnnrqKVavXo3rusyaNYvbbz93ch8XcAGgeH9a2RSMUAhn1w4C82eRfmULcsFiDDph3DzoqoWeOmRDHbI2jbQcCETQzDI4/Api1BJk1w4IWkqSS38PNwvpgReA9qOI4jHIUWOg6TgyUKe4t7F+iPhUIZbph6F2COWANQDJbkTFfGTLMRiKIQvyEPYgomQawUmNWIZHfEcN4aqIMrsQmsrYagJsD6LZiKFOCI/NqHPYpz1clTEtqoBUTGUvRQmypxnZcVyxBVxXUR48F6wkMh1D+COI4nFITUfUboGL/xS59RF4/VGYcT1Le9tY89hDXLPq88RTZ9qfNnb0cP2nVvGpK3/O1cuW0lBfR31jE41tXdS3d9LU0XOGusMfiiVLlvDoX99K6Y4n0RauQhs5A6+7AWnZiKEupJyMiDVB3iSEGUKigRlC9tcqKTLNQBRUQTQPOmsg1o9ePhKntxe3qVEpOR16FXHpnyDX/Bfi5n9Ei5ZBMITX2we+ICI+CKEoDCbAs/GNnoTd3Ejqxd8RuHQl/nlLSW/egF5q4rXU4ax7BPOWO7Gf+QWabWNMmYuWVwrhKLgeXjKhbIF7O7GP7Ebs2wKAlluEGDkJbdBGdB95S08YEI4qnnPmfJD9Hdgtx7BeeQoZG8SYNBOCYYTPjzHnMuz922GwE9IJ/DffQur3D+CfvwznmUeQ6RSBz3wRa+1qtKIyrP07GfjPfyK44gbCn/nzs2Ye3Z5O7KP7cZrrQXp4vV1Ym9eRfPKX5Pz4YfTI+VuM9odCy8rGP2cJzFkCZLjSR/dj7X397KY0loUxZiK+WQsRpg/fzAX4Zi7Aaawl+fsHEaEI/qUrP7Jitwu4gI873pGze8kll7Bw4ULuu+8+br31Vv7yL/+Su++++8No2wVcwCnkj0H0HMZY/imcNb/Cd+MdWKt/BjNGo29+EGfIwYvbSAJIywbXQRctmDPzEJ4fzRdGpj1kuhctPPkdPGrfAukhbVcFOu1HFDWhqBx6O8EfhtiQ4uzigRlWvN1IASR1cLoUdzSnQKkPJNLgEzDYAoYP3/xL0I2NxPZXE0qvwbjiTxH7n0GWloInoK8BsrLAGlRLpqausriaAbqG7O+EoxtBDyArVRZO5BYgq20k8pSOs+eAlVa83cqL1HaFo5GaDkdfRiy6Dbn+Xqh5DfLHsXT6RF556Of86Te/y4Fjx4ftlsdfeInHX3jpDx3Zd4VNmzYxt7aW1X91M4sqpiAqpsD+l8GVyPQgmuUgnbQqBtR0yB6F7DkI+ZOh6yAUq/18Op0AACAASURBVEIvbcZKvHU/Q5+3ChmMoG/5HZ4bwm1thlAUY9ZKKBuLt28D2vRliKwIdPcolzjPQkRLVRZ8sAfz5q/CU/8HYZikXlqLf2Gc4OXXk3xpLXqlgddUjb3uEcyrbsdZvxqvsRrZWq844IEgIpKNZpiYRQWEFv0NWlE5MhnD2vQs1u4tOIMDSp7tdBWOt4MRhCyT1K7X8bU14rvkOmR7Pea0edgHdyJbavB2rQcrjT5lDvrW9chYP6kHf0Lkru9gbVkPuol9cCeJ9U/hVu/HmDYHLZqLFs1By8pBRHPQojno+UXoi5af/GkvEcOuPkji2UcY+F9f/MBd2c5naJEovtmL8c1efNZtpJS4dUdJPvUwaALfnKUnOb/GiDF4/b2kNz6HTCXwLVyOUTHywzuAC/jjghAqGXKu9v0xxbsqUPP5fHz1q1/lxhtv5N/+7d947LHHTlIbLuACPgwIIZDhcoTVh7HoEtw3XsW37Gqsba/hZBegZeei5frQfDqaX0cE/VgNTbiHtqGPn4Nn74GxK6BhHbLQQwSyOd15Dt0EYZ79Ys8qgZY3IJQLnccQRWMgFkOm+9WNJxBUGWDdVCZswXxlMiGDio4wdj7sWguODxmIIobaELmjkf3N6BE/kSWzSe6rJvmb+zFz/fi941AxQZlhSA8RyTulnSxdZTHsCEQoCvmVyFQMGncrDd68SuW+lIyrTLEn1fccW3FbuehUv+ZXgaYjD69HrPgL5NM/UKoDDd3MXnYlr98X5CfrdvAv//nfpIbJ8n6YaGtrY9k/3sd/HWvgyz95BKIFKsvpppBDcUQ0F2ItEB2BEBqES8GJIV0LnBTCCCACWYjCEdBTh0jGEaEs9LEzcN7YityzAypexJh1NXLdfTBuHlrlKNzjdUgrjQBEXimy9Yjqy6EezBu+gnj2PjB00q9vwhvsJ3jlzaReW4coldBRi73+UYxLP4W39zWkFQfbRg6CHOiBSA7O0CDi6B4VAOcUYIyZQuSyW5QsW0cTMtanJA8TQ7yTUqRMxXF6RpLevxf3dw9gTp+DlohhjJ2KdXgbXv1e/ItuIP3C45gTpuO0t2If2Eny+d8SuuozGKMnIm0Lracdq+E4dk8X0jCVFJ4QYGZcGs/Qo5YIw4c5fhpWfw8DP/om2d/4wTk7F/7YIITAGD1R9a+Vxtq5ifTWDej5xfgWXIaWk0fwuluRVpr01g2kt6xXJjNnIDP+mo6Wm4+WX4SeV4SWX4jwBz/UY7qAC/hjwtsGu3v37uXb3/42DQ0NjB07lu9973tnUBvuuusugsELF9kFnHuIsunIY2sReWPRp9q4ta0Epk2E3CJEOomXtsF28aQJCQ89PYgbrUAeeB195lL01sNI4UO2vAHB08Tz33EyKyGQC2UzoP0ABLOhsxZGjIODG8E0wU4AGjLZryrlO45CMAwioori+puQpuJXYjugozJ1dgxySxBpi9C8KcjWFpyqpcRffhxquvCPrsKwYsi+zoy1sYb0PKTj4lmO8gTJthG6hsgvg0AU2duM8Fy1HRJhWYALjgtOAq+/BS3nVJW9yM1ws45vg6v+Gvnsv8OExXivr8F/ySr+NhXj06s2c+c3vsm6des+2EF9j7Btmzvve5adxy/jv7/zD/iD2ci+GKKrAQrzkNbAyeEUwQK8nkNQMBG6DkBpxsBh7i3IZ3+EvugLSM/CO7CZ4K1fIvnEr5G/fwAsD1E1EW/7U2hVYxHmRmRsEKEJCIWRmoHAxt30OPqln8O4+s/huV8gfH7smiPIgfvxX/9n2Pt349oWWn8LzvrfYM6/Cn3MNLTiCqRt4dbswW04iuxqRjoWckhpAltdbYit6xA5+WdVYzgrjCB6rkdw/iKs+jrs2hr0phrk0X3K/MTqR5s0G2fzOsJ/9nfYh3bjZedgP/8og9s3kPXP9xL53F0kn30Eo2I0crAPOdCjNJM9Dxwbz/OUzrNhnCyYIxBChCNYh/ZgzrsU++VnSLz4O0JX3PSBjv/HAcLnx79oOX6W43a1kX75WbxEDHPyRZhTZhG45J11jqXj4PX34PV04tTX4O3ejLTSfFJNJYzxU/FNnf1RN+NDwQUxhveHty1Qu+qqq7j11lv57Gc/y9atW7nnnntOFqVZlsV9993HM888wwsvvPChNfhsOB8I5Bdw7uEdfxmipdBVDVUX425eC4DIKUQrG40oHnEyI+Ie3YG7bQ0EPeyaZvxXXI/IL4W+g8OKLpwVUoKhIQrGQ3QUdB4FbxASg8h0Agb71F0iVAB2XDluWQ5i9HxkohfirSBSyOzxcHSrMnnIiyLMfHDTyFAZHNmkNHcjRdBwBIorkN1NpOws3Nrjap/qSEHT0AIBRMCPFx9S7mWpoZNFa5oh8EUFek4EUmllER3wQTiIGJv5h1A8HS1v9JvubvL4NlVQZ6WQ21bDhKXQUo9Y+mncFx9Cv/ILPPLbJ/iru75C10DsbbvM5/MRDocJhUJvegQCAXw+38mHaZonn3VdR9d1NE07+fyb3/yGzs7OYX9jzoRR/PYbn6PC7lBZ3YXXIvIrEf4chF9xH6WbRg7UIew05IxC+JWslLvpYURBFU5dHd7BV9HmrUDPCmJtXIvTm8A3ahRGRTkUFmOt/S0IDSMngqy6CHfPawgrjhg7Ez2vEH3FHSAlzoaHcAdjONWH0fwG5pW34Q3FsbasRUv1gCfR8oqUqkI4GxHJBV8IEYmij52GdB2czWvwulvBthUfu7ASLSPlJoRAhLMQ0VxETj5aUTla+M0yWTKVIP3SE4rHHMrFPlYNUmBk+7DqGzEMB2PiLDzLQxt3EW79YZzGOvAHkAPdeB2tRP7q+5hjJ2Mf3Yfb3oxMpyCdQto20nORQ/3Ivi5lEGKY6lhMv2p/fQ2u7eBfcBnWC78l+7v3YpRVvYeL7ZMJ6XnYh97APrgbEQzhm70YLTQ871mEo4iPeRHg+YbzIb440Ybnrp9AeeQcFajFLK56+ujHMo5628xuT08P8+bNw+fzMXv2bPr6+k5+doLacNNNn5yZu+w/jrQGPupmnBVCM5TKgBF487NmfHwkbsrnKweyaCH0HMO8/LazbqpPmAudNTgtjfgmlJN4cjXBq6/GmLdKqRu8B3j7H0P21SEkUDAOemoh6CGcBNK2wacrmSZbqj4300gzAm4nCD/oDmKgDel6yvrYdYEByBkNXZlCt5xRiL4aZCAA0o8WDBLK9sPCVQgzaxjbaIn0PER3G7K/V+n7+nS81joSzz1NuLIU4irjjGEqB66hLrSZtyFrXsCTHlp2uSrWEzqMmo/cvwYxcRly1Bw4tlW998Z6jItvwXnpMW677QtcddVVfP/269jVZ1FQUsaI8lJGlhVTVVxAVXEeVYW5ZGVnIwIRPBeVgerqRFpWJjv97vv9rumjuO0HP2VHde0Zn+08WsfF37qHHf9wEwXBpHKy83RkrOVksCt0PxhBZLAAug8hyhcAoM25EW/D/0GfuwrZ24S7fT36iuswp85Dbz2O1ZPC7dmFUVqCMH24cUsZY3TVZnRZTWRrHS7Ayw+jr7gD45LPIF57HDFnMfbe7djrHkObMBv/1beT+v0vMEpykIN9MNCD5zYo2oumI3x+3D2vILJyENlFmJd+Cm3EONydG3DrDmUyduAhT1qOA8pOOBiFnCI0w0D4/WglVfivuh332H6cV5/EN2oU1vHjWJ0D6EEDdzCB1nQI3w1fJbnuKQKXXYdWWIpz/AjSy0fPLyH2H3+Pb/Yiwl/8FuaE6W9/XfR2Ye/ditfdpuyarTR0NJPesgFzzsUM/MvXybnnyZNylRcwPISm4Zs6G9/U2XjxIew923CsYdRQpETGh87Q8xX+ICIr+5Oa2MUoH4kxeuJH3YwPBxdSu+8Lbxvs3n777Xz5y19m7ty5HDx4kFWrVp2xTXn5Hy46/scEIc5iPiDEyWVm9RDAW/4+xyeSWmq0lPSU26NeuxZId/hEptBA86lgRzfVa83gI71jGkFEsEAF7sNA84fwpFRZ1Pbqd9ydNvd6DH0tbt1BAheNJ7n2OYLJFCL4HqrFpYcoHYmI10K8DRCQNxL6NJDtUFAC3S3KySwdV30Y0KFmE+SXQ7gYYvWgJ6F0DHQ3QMhARgyEmwbhQmEVDLYqg4uSQmhrRYazEckuCIyA/BFoZ/AkM80rthHxTmTHUWTzETTdUEPoKIMNKTITIdeG5BCkOqFyNjTtxBvsRKuaA25a8VwnXIY88hJiypXI5CAc3w4FY5HdjejTFuNueYa8xdfzw98+j/XIjxChkDpezQBNINOD0JLAam5EeDbCZ6JFo5g5IZBvY9xxFozOLmDjE7/g6//1a+6///4zPm/q6uN7a3bzk1sXIdqOIzQHokVIawjhy1IbZY2AngMQyEXGWhGRMkQgDJFiROcRRF4lRrwT6/Wt+JYsR2s4QOjWr5J+dS32gU3oRXkQS4HhV1JngQjCr6sMZ2sdrhDwyiPoyz6HvuA62L4G7ZKrsV5bi3d8P7KnncDNf0762d+AHoFQBGEaaOEsxe92ksjuNryeDtBrEcf3o4fC4A+gB7MgL1+1NxhBBMOIYAgvnVLSX52tyNpdeBknNtexsavfIHjjF9FHjCf95L2YOSHsfpA9XcpDIZFA5hYjDA1PN5H9PfgXLMfeuxV3YBBj2hyc6oP0/9Vn8M9ehDFnMUblOAhlnzFp1vIK8V92PYCyq332IWRPB1pZAU5dDRSWMviPXyT3Bw+857H/pEILZ+FffPl7+o6XjCNjQ+eoRec/xHlsRf1B40Ks+/7wtjQGgMOHD1NfX8+YMWMYP378h9Wu94wPY5nB6zykAslhkQl2yQQkJ84amSkO8lyGXzt/63tnO9ukyr5phhI7P/21biLMMJhBMEMI/d0FFdJzlCi/m8o8p9XjI4NEpgeUokEgDxHIR5ihM7byBlqg57Cyvy2bh4i81UpXVT+f+MfsHdqIzC3Be+0hPEfHrusA33vU2ZUuwWtuRqTrIVoMvhxVAFX3opK4aqyBwkpIu6ALpX8qBRRPBkOHrqPgsyBhIDvrIByBnBBCC4MZQBKEnkbQsyDZAb4wuD6wexF2EkqnKFWFtwrd6yYilAu5lRAtRmg6XqKfxH98BbMkH90EEilEYRFIB7JzEHNvRUTLkIkOaNqhDDNGL1OFR24a2dMAsV7IrcJrOQgdNdDXjZiwCCmCoOnoU96dmcC77uKM05pMxiARU8/JIWQqgZBp/i977x1m1XVf/X/2Puee26bc6QWmwNBm6B0BAsmSULEFyHKRbCu25MRxYqfYjmM7vzexk7yvf4n76ydx+dmW7dhWtQpCQgihgoRAooneBqYwvdfbz9n798ceDSAGCSlCIjbreea5zHDKvuece886372+a8n8cfzsub18/vOfJ51On7Vupt9H/T99iMjkSYiF1yEqlqG7DyILTlcldXIAneiFwRbEuCUIaaM7G1GHnoapV+Fu+g3S5+HFFdaEqYiOOuyP/TPD//AR7MnleI2tOGXjYbgLL3cKIjmMsEC1N4FlI8ZVYZdPQS7/EPR34u3YgC6eRGrT/QjbhyiqRBZXYJVUQHYuaI1qrcc7dQKv7ZRJElMeOp1CpIYRygXHMZG6tmMq8yMPokK55joIhhChDGQwA5FbiMwtQg31kXri14iapQQ/9FmEZeHu3Ex62+Mkm9qxS0vQrQ3YsxcjC8pwewcJ3f5Z0ls3ohIxQ6L7etC9XYhwGK+tGd3Xbc6PUghLIjOz8E2Zhq9mNjKnGJFTZBL0LBsdGyb2wE9JHz6AnFSD19OJaqnHmbeMzLu++I5eM5dxGe8WLiUZw8a10y6qjOGGR4/+8ckYAKqrq6murn43xnLpw5LmZyxoBSp5mtiOds5fgDhUyNPEdczX16qt2jSJaAU6bfbnKlBptJsa8V5NmQrvaziTGNt+cDINufFngB0CXwh8l0Y+PWCO13ALergF7caNFMMXBn+OqTwCMnscqn0XZJejOw9DaDlEu2GoHR3vN9tRLoQLEKWzENOuhJfuxVr+UcQrDyEmlhnLsAsek8Lr7yH24O8IXP9+rP5WKAiYKq/wGV/crFzobIa8MtOsZjtQNhvq90JuCYQLINYKGRL0OOhuhbAP7XfNuehqglARZAXhRCtk2jAYhbRGZxcgkn0jQQTq9LiEsRXTbtSc+9ZDJmFNSJziCMnWHkJVxWiRRCcSCFtAKg3HnkJPuhZZMBldU4aufQpduxGKa8CfDQWTYeBFUHGT1DX9WhjqQu/bDOU16PZGPOlhTZp37qHSpokJrYxzwHC/cRTobkG7aUZjgs+sUgthzm0obCrugTAiGIacCqQTwDt5GHf303x6Ujkzn3uWa1ddTywWG119KJnm3j0N/HkkA9q7sEuiiFABOtqBCBeZXfizIdaOzp0CnfugeD6isAKOBBBth7Cu+QTqoX/HmrcSr7kdK9qP6qhH5OSih2LG1GJoCCkEcvoSvM2/w/+FH+H+1/9CdXehm2pxhcDe9STWwpuwFt2Et2MD/tV3kt7wW3RLLWJiNVoKdHOdIZCpJMIJYldMAQQiOwLJKLqvE6+nBzXQhxqOGUcJd8RbeaRa/5r9kJDyDDJsodMp7FlL0YdeJnZPgtDtf4298FpEQSnu3d9GtTUjfAG8Y6/iXHUr7rOPozrbcK68Ea+5jtRLm3AmTMG1fXj9vYiMbBN4YNlgWQgngE4mSR4+SmLPbmQgiDNjDr6ycQg0OH4CV69Gex7p40eQ5VWQu4TUji307H4J6fMhS8rxzVqI/8pVWBlvP1nvMi7jjxLyIlqPXaztXgK4IOuxyzCQeRdHE6SVN1JVTZ3xY4irkSUk0fpsk34xWgGWaOkgHP8IMQwav007iLYDRhOo0uAljO1RchASXeih5tP7Ue7p+rIQvKcyhkAEypYhMsbBYD062mFiYNMtkHlGpnxWmUlC8xLQuB3C+ZBTgSiZdbqie+JZ8NKm0l21AD3ch5wwHRkdNMESFwrlQaIPOaWC+MYn8M+bh00tong64AOVgNwiGOiBrDzojkJ2GUQ7zSfMTUNJDdS1g04hLMdEAQ9HjUVYYtj4505eBLsehdnXwc5HobgCAlXGUiyUY6rVZzw8aZ0CNwWJFKK7wTwoZBbCxOVYWRmoEx0QDEA6BUODkJNtlh+/EE5uRikXWVQNk29An3oZffIlGDcbgkNQVA4nd6Orb0A07zehBLOuhUNbEMtvR734IPTUG8u1dMpUZRMxdCI2oik10h0RCCOychF5uUjb/7qZjjOvs5H3peMQj0O82/yajGJVLUDMvgLvpXUsaN/Bx69dzs8e23TWKfrZK8f5zE0LoK+dxIM/huwSiHchi6cicwqQkTyIFCLiTWjLgVgXIlSAKJoAqSiWI2Dm1aj9L2LPXYF7oBue+RW+hUtxn3sC4XfQsWHIDCCyIuhEAulzsD/2dbzffh1voB/dcIS05yF8fuSca0YJr2/NnaSffgB3+1PG0cG2wfYZ39rxVVhVMxBF5eieTlRLA17aQ/qzEOFMYzemRnS6Wptjpo2zhrAECGU+ssqFdBIlFO7urViTauDUYWJ3f5PQnV/DqqzBysnGTafRjo1ID5Nqa8eOZBHfcD/h2z+LNX4igbWfIvnkfSZGWIIIV6ARCNtGI9DJOAz04UmNisdBQ2L3DhIvpxAZmQSWXoUTasWZOgs1OIDu7QRpE/zQn2IXl5I8cRTv2H4SzzxGfN1vRh70z1NAeJcgbB921TQC19+K8yYa5cu4jMv4n4nLZPcSgJAWyJEq6/mWeaP1ea2ilgQ3biqLiT6EGwc3Yf7vrBVscGyEnTdKjPEF0VbAaHffS/SdhCMPmYjc4vmIzAror0WnBiFcjJAj4yucBSeeME1ZdgDwINYCsZbTxL24Bpr3QMViROlU9Lb7Yeb7oHk3+N6CjEF52FUKt+UU/injSdeewGsL4F8gIHs8on/I3LD9fuhpAy3QqRgi1g3lc+HEbkRGEzqQA243ZASBYmhtgOwskElEKIJu2I6YdjW6fR+Uz4G2gxBMwso/AyeIdIJGtnBGFV7Fh+DA0yb6N5wNyRhi1z2jjWBaWsaj2FNoIc2/j75kqt31W1AqjSyZhai4AlU6F44+aazUwvlQOhVxaD1MXgIFxYjmo6hAEF6+Hzl5Jqq5FWKuiWR2ssEJITNGpDGa07ILS0OiHy2lqXifOUOhXNN0pbwzwhN8xr4N0I6NPrYVecXtWEtXIw6+wF98Mvccsru/uYftte0sLy7DXroYOXElKt6H7m5AeyFUTwfegR2InBx8UyrQPUchmIuYtAj1yiOQPoB9zcdJH92KaqnHWvFBvMd+jD1tAdp1kcEAytVYoQjiyFZz7NsbkMWV8NGvwX3/B28oBk3HSbtpbDeFteDGUcLrrLoNd8cmc90pjfYFTPTvod24r24159SyENkFyOlLsCdWI7Nzz3tJas9D9bTjtZ5CNR5F9XaiY0Norw/p9KIaahE5ecjhHuI/+UcCf/YN7Glz8VK70b0dICWJh35M8H03E7xxFbEHfo5/xQ3YlZMJrP0UqV1bEKFM7Op5yKIyhGUZl45UEpIJVEczbksDur8bFQqgPI12XWLrH4DVH8RZ8D58Hc0k97yCVV5FYttmc56lwCosJTB7IXbVdEOi3fdOOqVdj1TtQdy9rzD0w2+A5yFsG5lfjJVfNOY6omg8wTW3YzmXLTcv4z2A4CKKdi/OZi8FXCa7fyAQQo5odoMQPPsmOdb1q7U2VdF0whDkxADCbTcV5reSLvYOQ3spyK0yhvrHH4ecckSkClJD6FinqfgC0rJR/izw5UFO+Rnrp2GoAwbbIRU1so5UFOGEETOvRde+jJz3/rc8LnVqF7YQeF0dOMkkri+H2JM7CNwcxAKEm0SHs6CzBXJLYagNcieYY+yz0L0tULUETj4L9jBCS3R2LkSj6CwHQQhig5CZBXUpqCiD3izwonDwGUTZjNGqu37ti05IRFYBLLzFNNHVv4puPwEV0+DAY6YxqXMAx48h4skUhIKmkn/0BcTUK6FxG8pzkePnIX0BmHkLumU/OtZjquyBMBx6zmiGp12LVXMD3qafQ0sdMhwGlQYvDmRCKILMHQ+5ZYb8J2Po2AC6sxHd1QSDPYAaSX+zDLEV9og0SJwmv8o9/YDmJiEZQzkPIRd8CDn7WmarjSybP5uXdu876xz95Ll9LJ9VZaQd8X5kMAcVakVGJiAqp+Kbs5TUtk249U3YE8qg8yCiaDbC54fMfHTPKeQNf4b35P8HrUeQFdNwX9qERqK1QisNoWxoPY4oLCP14Pdwbv8KMn8crP0bePSHeGh0RxPpRAzSKeSS1acrvLd+Dob6ja9uVws6GUbn5xuNcjqNdoKQSqAOvEhy1ybzQBDOBcdvSLI/iJWdi8jKQY5Yj1mF42DOaf20ikeJ/eDvEdFudCKB8lJYRRES//lV/B/7Atbh3bjCRqUT+LP9JOoasXILCH3icySefgS37ij+qz+As2AlamgAr/E46W1PmYo9gGUji8ZhFZTir6pBZEYglcStP0r6wA4Y6Ca2/lEsG/zXrkVHh0ju203w/R8hsGwVKp3CrTtOuvYQ0cfvNc4C73C09FuCVgjAys7FN3EqwvbhxmOotibczvaxV6mvJbn5UYTPh109x1TFc8/tG7iMy7iMSwdvSHY3bNjAtddei3PZ0+8PDkKI01Vdct50+XcLAtBuAnpPoLULfU3oWJ+xWBpuRYdLT1c2i2dD3Qsw2I5Ox836lg8yi6BgMvrUVnTpPGjejZi4ApGZh5YWeqATkV34lsYlyxegLAeLfahAEOvECeSyVcTvfZDAklnY4y3IyIHhQRgehowQlM6E2s1QMReOmuqddrLB64WiGkTHQXRbE2RkQCqByC9BH9uCmLcGvft+xPhqdP1u8GmUP4Rwk0YykE4aEphOQvcpoxkG49laNd9UtpXCycsg1tKDU5kLfgcSCUgGYHwJdLehj7yAqF4JLTtRXhpZsdgcw3GzYKgT3bADMfl9aGmbCvmue9HKQxTmoFrakeNnI/LGoZQyMorOOrz2OogOnrbHktI0WUXyYfx4U5l/TV8+qt2VnE8+o1Nx6G5BH3sVZQWQ05Yg5r6fz655/hyy+9CuE3y3Z4DidArdcRBRuRyRXQUDJyHX9B04S1eR3PwwXtMprIJsMwMybRnq5C4YfhVr3hp0OBPVdhK5cA3e73+AjETQiShIHzoeRSSiOB/4NKmn7iV137dxPv41ZGEFXH8XPP0rvKEoRAdIv7wRKxnHXvlRQ3ifuQf8IUR2PrJ8CiJ/HCKYacI/2utRJ/ahokPGPSMzgrZsxGsShtQAOt6N6m0E18VNpyERNw8O/pA591kRZHE5wb/7PvHvfwk52IkuqsJtrMWunktqw2+QeQVIT6J6OlDxOL6JU4i9vJVQIEzw+ltJnzhM7Hc/IrjmE8jMbOSMhTBj4enz4bmo9mZUdzvqVC16sN+QckCGMrBr5qH2bGN4y1aybBvnmrXodJrkc0+gh4eQtrnlWAEHa/K00XXfM2ht3lN/H25LIyoRB8uHDIUQkbwxVxG2DxkMkmpvxq07Tv/X7gLLxiosMdKTMeC/4hoCy1ddzHdyGX8kuFzYfXt4QzeGefPmYVkW119/PWvXrmXBggXv5tjeEi6FbsnLeOehk4Potj0w2Ax5FYj8WabZaASqZRsEC8aM1tTDg+D2ACFE/hREKAftptGvbjhP9XpEE/l6eGlE1UJEYSWq4yjU70ClXLyD+xEFGSRPdCBxca5ciox3QxyI5CAyi836xeVQ9yoEIojSanTTS5CVA/19pqolkia+OLMQ3d+HKJ0C/YNohiCWMNpYOTLFL63TVVFfAEQIhM/4BhdNQiSj6K5G9KGnwPMYPt5BxrTSkWOZgoAfWTwOnVMMvV2ARExdAe27oGwpsnTm6aPhptC1bCRnkgAAIABJREFUzyOyiqG4etTlQw20ouu2ow+8DD7HeARnRBB54xC540wi3BmaaDOLkDazCModbaYS2ju7mXOsM+Kl0EdfRIeKoKUWciuRM+aT9OdTUTmVrv6z3VH+dfUivvbVzyOKxiHKliJ8AXT/CQgVI5yM0fEkHv8tdmUhVnYQUXYleut9UDgeUTwFNdSPfvrHqJzJqEPbEdXTcffsQYbDyEAAKZP4/uaXuPu3kH5pA0K7hvBm5+Md3oZ6ZT3ewBCgIJXEmnkF9vV3IaSpEDPYYxr2ulvQ8SFjtOL4ERNnIYonQHQQr3YPqqvVVLs91ySW+YPmnPuMVaC2fYj4MHqoDz3Ua2QMvZ0obRP8/DeJ//Ar0NMC05ej9m1BRPJxlt9A+tXteC2NCO1il01ELL+V2KaH8eXnEf7UlyCZIL7uNzhzr8CeOmtUUnIhSO/ZSmLzw6TrTuC/4moC1VPwCJGuPYTb1GAa7AAcP/aEKTgz5iNL3rvQCWFbyIzTwRw6mUB1tZE+eQyvv2vMdVR3J+lT9aAVMpyJFclFxYZx25pG/LNfB60hZkJfZEEx4Ts+f1kb/D8MlwK/eG0MT91azbhM/0XZR8tQkusfOvIHyaPesLK7bds2Nm3axGOPPcYnP/lJiouLWbNmDatXr6aysvJdGuJl/DFD+LMQlVehDt4HnoeOtp1FdkXxfNNo93ooF/xRGIhCcRm6ZQ9i8jXGBmrhmrc0Bq0Veuu9EClCFk1D+wLI41tg7kLUnq04FUUoL03yxR045RFEQTmyqw0tBGLqtdC+Dyrmw6HN6NkfgJa9kOiHghpE10H0wJCp4sWHEflFULcXFt0GL/0OCosR1deMuEdoTjdyKVPR7TkJ6V503IYTTWhlIUK5o5pZaQmUkEjlGWlBSqETSURfBzqSB/0D6GMvICYvg8YXUDnlyKA5vsJ2YNp1MNAGJ7eilIvIrUTkT0TOvRXm3nrusUrFYKgToqcDaM56fBAWaAlYRms9SuLH9ncWQkBGCbz4Sxg3GdIp1MG9OIXF3HXTcv79ng1nLf+zl47y5doDiK4uZFs71vy1kD0B3XMIkT9rdJuB93+cxEM/Q8ysRHYfRlTORidj6LodWHNvxpUOMi+Asi1Ie0bCoFx0Io4IWahUAnvWSohHSe/eQvq+b+F87GtYNUuN7n3rg3jRBIQcvAPbIDqAfcvfGHeL7AJEdgFUzTl93BJRdN1+vCOvACBLqrBW3moILqCTcfRwv3G3GOpDR/sRA32m0u6zELkFkFuALhyHOFVL7PtfJvS5/0385/+CPvAicuEq1K7NeCcOYQUddHY2amgI1VZHeN4S/HMXE3/uCfr+158Ruuk2grf9OeldLxJf99uRB8OzHw5lVg72lBlY4yecRYZ985bjtZ3Ciw6SeHETvvHjsCdVoKPlWFkREwCYV4TIysXtbCO59WnUYB/vGTzPJMGBSbIrKceeXINv6gyc81RphT+IcJyR1LO9xLduQg0PY+UVGEnM6/Daw56QErejjaHv/gNIC7tiEhl/+ndYBSUX9S1exh8YLhvtvi28qc/ua+jp6WH9+vU89thjHD58mNmzZ7N69Wre//73E4lELvY43xSXwpPXZVw8qN4T0LoLIuMQJYsR1ptLa3TPIbQvB7r2Q7AYES5EZJe+rf3r2CB670bEFR82coSBVvThTei2enR+HupUMzqtcIdj2PkRdHM79lXXIjOKIN5viNrRrVBYbRwMevdB+Qo48TxaKbAUIjQOHB94oG0bkVuNPvE8wheA0HmmVIVAh3KMDd1gE6TjaGnBgZ1gQbp7EE86BLIdcDW6fDq01ZlO/oJCE4QRjYHyEOU1MNSCWHDXmNU8rRX0NKC764zTQqQMkkOn7d4QCF/QOEKcrwFQj0zLK/eM1/T5K7vJKCKzAOUpOLgRlETMvB6SaRqevodJn/8+r/8Ke+RLt7H64x9GxQbQXpaRiGT4kUWViMr5ozIY7aZJPPAjnPmTEJVXwo4nIK8IUTEPFR1Er/82XspCp5J4PT0IS5rO/fwIYt6N2AtvAsDd8nvSh3YiHRvnti8jMiKoxoN4T/8KL55G+Bz0cD8iKxeZW4woHI8on4YsrECEsng9tFbotjp03f4Ru7YzzncwA8IRREa2CbcIhiGQAU7AXAvpJOmNv0Z1tuL29BG862uk7vs+7qmToDzk+InY4ytItbWiTh7HCknk4jWE1twBgErEiP7qO7jRNIErrsYeX4nwBxCBoCF5fj9CCLzeLtzaQ3jN9aA1IhDEnjwde8I0sG1iP/830o21qO4eMj/8YezF70dk5hrJRk8HXmsjXvspSCXf2xvsiMOFLClHZOXgNtXj1R9DDQ+i3bGvSTzPENuCEpzJ07EnTUc4Dl5v12ja3dn7ALetieT2Z/HamtBuGuV5EB1EpBIIx49v5kLCn/oCVvBy09uliEuBX4xWdj9Uc3Eru78//AfJoy6Y7J6JkydPsm7dOu6//37i8Tj79++/GGN7S7gULsbLuHjQWqP3/dp4wGZVIDPf/BxrNwGDdeieNsjOh74OxJTr3ransG4+bOzLpi0zvw93o57+vxAOQyQXkgnclm6EG8NLBqGvHd/8OSMJZQMQKoSDTyOu+Wv0nt9C0AaZB4PNaJ2ElEJEiiC7EH2qHjHv/ehDWxHFhZCRP/aglAeeAhdIRCE5DLEu9NGdIDQ6mSLaMkRGRQ4oCRVTEZWLUYefga5WREYGurQMEYuB0ojCYghkIWtWv/Gx8FwYaIFgNgTOTdZ6J6Frt0DJdPShzcbRoXE/3PRleHUDa7/0TR5/5eBZy18/dwpP/OxbiLAfIuOQxTNRyTi67jl0zIc9/3Q6lU7EiT/4QwJL50LMhsIJ0HEUOetG3P/6KiqdxmvvRGRmoDq7IBjGV1mFUGnsO/7VbENr3M2/wT1+ABkK4Xz4i4iMbFTbSbzHf4RKeRDMQA/2Qmae8cd1XcB4IgvbRuSVIGdfjSybMuonfc5x0Nqc4+E+dHQA4lF0YtjYviVHdNvJKHLWStxdm1HdnXg9vfhv/XNSj/wYt6kBa2I10rbAH8KtPzpiaSawV6wlcP2tCGmhtSa9bQPp1maIFAPCNLsl4+hkknOqvMEQMjcfkUzgNZ0g40/+GjyX4e99Ba+/D5wgmVcuhszcEYKeOZIGlwHBzBGHjvcIQqL9QXR7E+7Jw+j+HvPnrBxk1ti9DCKchTvQh1d3FNXXY2ZQsnMQTmBMORWALCjCnjgNWVCCHugl8dzjpOtr8fp7cQf60f09COUhQmECV3+A0IfvuhyxfAnhUuAXr41h00cuLtld9cAfJtl9y24MPT09bN26la1btzIwMMCcOXPefKXLuIz/JoQQ6MxSSAyB1YrOGPemBEvYAUMQyhbCqS2QM8lM++dPentjGF+D3vMEuq8NkVOCyMg3bgXRYSgYj1Ap7HkLoW4fXsMAeC7p4/U45QsQ0Rhk22Db6K4GyJ4A0VqYvhL2/AY8DUEfemAQYVmIsinoY88j5q5Fv/grRGbO6QqYEKflxf4AhCOGRIVygByEPRV9Yo+xUZLCWF1pjXBs6OtAXrMQLBt94kUY6IYTR9C5hWAJ6B9GhIZQbQeRJTPOfywsG3LfJa1l1XL0oSdh7mp47icw72Z46rtw/d/y2RWPnkN2n3r1OCd372TSzGmgk+iiGUh/EF21HO/IFlTDYWRljXkfgSD+tZ8lve2/0AkHcWAX9tRqU1H+wN8i7vtH8FzsonyS7Z1In4NSGqu/7fSxEAL7mo9D+m7ck0dJPfR/cdb8BbKkCj74RXj4e6hkDKtmiQnb6GpGp5LGciudMr6/WqCeuQeUiwxnIacuQFYvRQRCZ+2HYIYhigVljAWtPLxnfot9xc242x8HKUiu/yXyylvg3u+hTtUiSiuQ2RFkfiFuXR1OSYT0y0/hdrQSXvMJZE4+zrL3Y7c14NUdNNp3B/A7iNzxyOJKY0fmMyRVRYdx21tw25pJDw4zfM9PyPjkXxO88++J/ec/4fV1kxgUhK5bYzyl4yYhTw/2QEejCcR5r6CUidBm5GYYCUBGBO0Lo92xXSJUewO6qwNf5STstZ/EqztC+ug+dDo1djVYK1RTA2qgHz00YP7mOATe9wFQGlV/BKU1KholeXgv8Q0PElt/7/kr3iPT2EJayIxM7KJx2FVTTVX9fKFHf+CwS8uxx094r4dxGRcB11xzDb///e/JyTn74XNwcJBbbrmFZ5555oK2c0FkN5FIsHnzZtatW8f27dspLCxk9erVfO9737us3b2Mdw9ly+DwgxDIhFQ/+C/ARSJ7ImKwAR0aB8kedCwGuRPfUtPNmRBzbkBvvQeW3maqOAWVJiXNtiE1YkHmc/BduYTk+iexlUtq4+/wX3kjxHthwiI4uBGx/FPoQ8ehZQf4sxBuD9rnwEAfekAhglmQVtB7HHn1Z0bS+ADUyKseaXwZQPe3QsNhdHIIhGXIk5SQToOQWCEfXiyNneU3wQOvPoacuxpl2eijz5rY2dgQOplCaw+RORUaX0RllSDDY8sn3k0IacGkFeiTLyEWfgS95xEonw+7H2bVdVcx8ddPU9d5tu7zp8+9yrcqy8ACemsReVMQThayohpv78uInEJEtqmWW5nZiKvugObteMdaSDV2wJ5vYc2+ypB6NDoQGJnxFsbJQnrnjNFe9Ul48he4zadIPfgDfGs+iywsg498FR74N3RPm7Hw0gp8jgmVKBgHGdmoxmPormbQGi+YhT5xAHfvC+eqmG37dJOakObY+APgGJkB4SzEyttQz/4We8UHcbc8bGQHO59F+v3oVAox8wr0oe2IrAKk0whTl+HvPE7y8HaiysU//0qc2YuRJZXIksrRXWul0H0dqLZG0sf2mLAUAH8AWVJJYPY8AguW0ffdfyS+8SFCN34I56bbST1xL8ln1pHauQVZWIpdWoE1cRp22USsiXNHHRouBWitIdqP7uuA16rlr4dPYMkkXvtxEj/bhMjIxr/8RuSEGeet7HrdHaR2bkF7aeyyKnwzF+C1N+OePGquq7QLQ/34J07GXrFqxDd9rGY3hYpF0bEo6YE+VGcr6ZYGUvXHwX34nTsQ/8NglVeR979/8l4P493BH5lmt6WlBTWGa0s8Hqezs/OCt/OG3zLbt29n3bp1bNq0CSklq1at4u6772bRokVvfcSXcRn/TUgnjLL8JrRhuA1xAWRX2EGTPlc8HU48BXlT0cc3QfAt2q0VVSMCWYZczL0JvedxxKJbEJUL0Y37YbhvxFs2ChnZiO5GAn/ylyR+/O9Y06aS2r8P3+w5iOIyIG2IqK8Q+k/CxBvh0DpELI4uzIVT3Wi7EVE+D31yN6JgqtHCjoVAhnFAmGjsobSXQkfb0Q17DCmWEic3RLJ9EDszCDqATg2jtv4asewO0xh2aCOEBPgS0D+I0kcQ42cgjqxHz/3YBemjLzZEMAvyJ0CsG/LKDaHvbcWauITPfORGvvof95y1/K82buVf/uVfCRx7Gh3Yi86djBACkV2FVT2I++Ij2Nf/yWhDkfRHUE4Ia+5irM529IRidE4pydZ+7JAfNRhFBPzGFzbgh6Aw1fIzbg7Cdgzh3fxbvO4uQ3hv/jOs8mnw8a+j1v0QbWOa9CwfZEbAc9GtjRAdRIazIbcIHR1EtdWZ6ym7wJBYJ2BIrmUjvDQ6bbShSnkwPGBcR7y0sX17+Unsj/4datuj2Nd8FPfZ+7FUB54vhE6lcV95DstSiNJMRG6E1ItPEbzrKwTH7Sa57VlS6SRu/TGCH7j9LPImpDRyi7wSmLFk9O86EUO1N+Id2I7qaiHy1/+L3m/+PXbZBALLrsc7uBMRycUqn4LbVEf64E5Su7agXc9YDb6HZFcIaQj4pBqcGfOxJ0xFZOQgMt78+8ECfFqj2upJP/8Y6qkHzkMWNDIQwDd5OnLxzajhGIln1qFTKXxTZ+KbsRZhWaihAdz6Y6T2vYLX3MDr5SIjm+K1aR1bCERpGXLGPER2LjIYuiTJyrsBWV71Xg/hMt5h/Md//AdgZrR+8YtfEAqdnuVSSrFv3z6mTJlywdt7Q83u9OnTueKKK1i7di3XXXcdfv/F0Ym8E7gUNDWXcfGhuo5C5z6IlCKKFyGsN78mtRuHwQa0zILeg1B+1VvzE9QKXfciomIJImAainTdHvN/lbNR674BoRDkFiBUCp1VAM0nEHNX421/ktTBw9ilxegU+K67AdF+Ejw/omYFuu5xmPJBOLre6DEzQmhPQ1s7VExCkI12FLJg8shgxBmmBQLCRQj/uU1O3iP/BIO95uanFMP1PWRMzIdALoyfBpmZ0HkKueIudG8Leu86SA+jZQB6OiE3G1k4HsJ5iJrVF1WT+1agT7wIhVNQ2++B2ACs/DTdLz1Mxa1/RzJ99hTy3V/7LJ/80j+gXrgbseLjWHlGvqJTw+ieWrz9B7BX3XE6XtpLQf1GaBuG2ddC61GSmx5AJofQviD4JKq1AxkOYVdWQXwY+9PfOXeM6STuM79DRePopiP4rv8TrOqzCwQqOog+dRCaj6O7m9HpJMIfQofzUD3t6FTCkEuf31iMSWn02emUiQ8e3dnI1/cZ50fbDmKoB2vZLdB6HHn17bgb/wu34ThuUz1Ca5ybP4k6tA0lA+jedlQ8hT1nOb7Jk0k/8V+4hBEV1VihMEhjzyWyIsisCCIzYjSq4cxzrgvV3Ya753msK26i9x8/T84/fAuZX0Ts+19FRQcRShk9bEEpIjsHbN/5G8HeBYh0mnT9MbyOVlPJ1RoRyceqnIyVO7YXt8iK4NTMRRaPv+DPhdfZSnrHM+iWE8hQAGviVOTUxXhdXaQP7gbLwjd7MfbEaRe8Te26eL1dqOY63OZ6dF/3Bb/vPzTYk2fgX3rtRdv+pcAvRjW7t02/uJrd+w5dEjzqjjtM0+zOnTuZM2cOPt/pB2+fz0dpaSl33nknVVUX9qDzhmS3q6uLgoLzJ8OkUik2b97MTTfddKHjv2i4FC7Gy7j4GG1UK6yGzFJk5tjaxddDdR9E5ExBn3wWIsWI4PmjWMfcrz8f6reeRXjVjkcQ1StQ238Jw/0wYSoi0YcunAwn9sCkJWDbpB+7H237kfnjUdEozvIViFMHEIs+gT72sPHNzZ4ALcfAG4RIDlrnQucRxLQV6MaTRqt5zidVI2yBDmcgcsoQuZNGq7Dexm9D5ylDhjyPaPMgofFZiNLppprrc6CkDE4dRV5xO3qoG33gCXDjaG1DbycU5iNzCiCnAjF+gbE0e4+htUIfehJdXAPP/BSuuAVO7OfOb/+K32zcdtayVeNLeHXHdsItu1AtR7Fu/qqpzAN6oA7dN4Dq7MBedMPoOqptFwz3I3yF6N4GvOFB3GfXgz+IPWEcqX2HsbKzse/4J3jiB5CRh/2xr587TuXhPXuv6R88uA37ytX4ztjPWFDdLejdG1GdjeYPkSLQFjqRgHTMdPqf8XUt/EFDOMOZEM5GBDMMAdq9GfLGIQY6oWgCwpZYy28l+dP/B7ejA1DIhTfC0W2I6UugpRbhhHBbmyAzn+DaO/A2/ZJkUydyXBVW1XSssipTYXbTqME+1EAfOjrMafG4GZddMRmZEYToILp0Ev3/9lVy/9+fYgXDAHg9nbgHd+CePIzq7TJuDGpsbey7Aq2Mtj23EFlajghl4jU14J46iU6fR0ucTqE9D5lXiF1VjV02EV/1HGRByZsSVa01XmMt6Ve3QlczMjsDq2wicuJs0k3NuPXHkMEwvtmLEKGMMbchw5mI4Pmj5S/jncelwC/+2MjuT37yExYsWMC9997LP//zP5ORMfbn4ULxttwYdu7cybp169i4cSNCCHbu3PnfGsQ7gUvhYryMdweq9kkTTBDIQhQvOG/3+pl4rbpLRjm6bgu8lal5rcCRULr0LMKr3TR693p02AdHXoJpcxCxLsgehx7ohkQMMe1K9P6tJA/XYZcWoaNxlOXDqShABEtM4ETnKzDzk3D0URP0EPKBE0DrPOg9hlj+F0j/uZ6fWmsY7ER31qH7miFhpBSipBrdcwqObgM0pF2SKYGIRnHmLkWMn4U+td904lfNgKYjyOpr0U170YOtCC+BirvQ34WoKENEikd8cDXkT4WS2cjzaJ5VYhD6jB0VgSwTOezPQlr2uWNXaePLm45CKjZ20IeUiNyJZ51jnRxGn3wJfeQlGD8ZXB+vHNjHsr/4t3NWv/Pma/jF79ejtvwEMkuxlnx0dP+6ex/qVBciUoRVZTx4lVJwcj20RxEzVqIa95F46JdIn41vxlQSO/dh5eYhpy3G+cCn8X70F1BWjb36b8c4Pwr14kN42Hg7n8aaWIM1YxmyqAIyc96QGCk3jT6wBd140BBH/XrNmjB9isICT5n/lz5DHHML0C11iOIqGOoC21iHeT2dqGgMt/EEGh/O4ithuB979kq8E3tRbQ14g4NoZeG//sPI/kZ0ZzNebw8qGsdLeqahS9qInHycFTfhzFs++gABkHhhIzIzGzHchayswR0cZOg3PybvX/9ztKHtUoPX20F6x/O4x/aho8PoZBwRzkCExvbZlcEwRArwGk/gtp0yVfeMCHZpOYyQ+rOgNcL2jUgWFiBGZBva83CP7cU9sgcGOrEi2ciiUkRZDW5r23lszDR6ePC0+8YIhM9BZGTxh52BdX5YZRPwTaq5aNu/FPjFa2N4+vYZF5XsXnfvwUuCR9100000NDTg8/mYNWsWCxYsYNGiRcyZM4fg27Dou2CxVENDA+vWrWPdunW0tbWxZs0afvCDH7B48eK3vNPLuIz/FsqXGmLoD0Gy30zNvwmEHUQpD2H5kNPe+kyEat4NLVthwnJ0/VZ4jfBqhSidjT623VR3pQXxQeNfO9CDdvKhvBKnv5dEQxP+Rcug+QRuazv2+GxE3kTo3AFN2yFrHOROhuPPQq4fIVJoO4De+jO84qlgO+ZGKa2RJDUJmaWIifOQ1hVmnMkonNoNyUGzjPJACJxImFhzF05PE+KK2xFZBaiDz8CRnTBtPrpuG4ybCz1NplYXctAUouvroToXEbYMGe2rg46DKNsPuRMg3geJQRPsoZXRo/pDJga4Nw0qDZ6LoWqva6yQ1kiohAO2nzFv1G4C3bwLZn54lFQJfwYUTELX7oDedsSUq1mU6GHZovm8tGP3Wav/cv0z3PjIOm6dMxtd9yqquxGZX2GIZmQylhXA3bMbkZmDLCxDSonKq4bEgZEADIWVEUQlkmjP+MnqWBzdcNDoPT/zQ9SP/xJ386+wr/3U2deckMgrPwQ7NiCWfQDv0A68J38D2jOOG04AkZlt4oNnXokcN2m0cVLaPph7rfkZA1ordHQQBruNq8FQL0T70G11eHX7EVMWoE8dR5RVQ28L2vMhLAtpWyP9LR46lIc+shtVOgl75lJYcA2px3+B6h8ktfE+ZGUNdvUKJBoZH8SO9psgE+Whh/rwnrmPoYd/bpogyyfhv+5W/MtXkXjifuzpc3H3voBz9QcJXXUjvd/8e4LX3Iyvsgq7pAxxCdlqWblFWDd8FG4wD0IqlcTd/wqqu23M5VVHM96xveCm8U2uQYQyUN0deHVHx4w/1mi0VqSP7sV6+VmsghJ8sxZhT6rBVzMfX818dDJBev/LpBuOIU6ux8rJMo2H52xMI0IOInNEn5sRQWTlQzATrcQfK9c1bjWX8a4glUrxjW98g+3bt9Pf309FRQVf+MIXWLlyJY899hhf//rpmS6lFIlEgoceeogZM2agteY73/kOv//97wG49dZb+fKXvzzmg/+GDRvo6+tj9+7d7N69m5deeomf/exnANTU1LBgwQIWLlzI1VdffUHjfsPKbn9/P0888QTr1q2jsbGR6667jhtvvJHPfOYzrFu3jkmT3p6F08XAu/HkpT3X3NAvNQg50jX+xwN14HeQNwmcECJ8ngQifwQhTx8XnY7BUCMit/rt7bN1HyTaoexKqNuKqFiMbjxkGqa2/tTcnPKLEF4KXToVGo+A7UPU3IDe+ziqP066pRvfjOmolgaE5WKVz0Zkaoh1wvw/h4P3Qc40aHgOsnIhewa6aRukGYnWxZBOpU9H7RaXmSqez49wMiFUhNr9IJw6AqmUWd7nY/hQMxkzKhCTlyJqrgalUK88bKp/UxdCey2UTof6nQih0ZZEJwX0NEMkB1kyFfIKIdpp0s/cJDgh0zilRqp+aFNhRJixnRXkPkJ2R4Mk0m8+ha08sIOQjiFmffS0DEFr1FPfgUQCseSD6P3PURu3mP+RvyaWOlsDGsnKYN++/Yxr3ACEkctuQ9iGSOiBerQdRu3fgY5HsZfchAhloo6vgz4XAkG8nRtwW7qQ48ej0y5eUzNWVjaBr9wNgBftR//8C7DwZuylHxzzbXj7njONZGVTzTEb7MPrOIXuaUcP9qD7OxA+HzI7D1m9CKvmitH0tLcKd+PPUcd2Qc1SdMMRxKQ56KZjMNhjTD0GuvGGYqhwIU5BNtacFciMCKqzGXvZjaSfuJv0yePo6DAiK9dMpzsBRGbOyO9ZqIFeY1sX7UNmhiE2gNfZgUq6hL7+CxIP3Y1/+Sq8XU/j3HwXiWfWk248YarEqZSp/mZFsCJ5yOz3kKgIgczKxorkYUVykZHc0crrm8HraiO58X68lnpQCpFbgAicp7KrFSoWw2tvQXsuIjMbq2g8sqAE/4IrsUrLRxdXQwO4h3aNJrudvS3QsSFwXVPNTyUgFTfR25bgj5XtWhOn41t03Zsv+DZxSVV2PzaTcVkXqbI7mOS6ew684fuMxWL84he/4JZbbqG0tJQtW7bwxS9+kfXr15+zzsMPP8yPfvQjnn76aYQQ3Hffffzyl7/k17/+NUII7rzzTu644w5uv/32CxpfIpFg7969PP/88zzwwAPE43GOHDlyQeu+4ad65cqVrFq1is997nMsW7YM+xKyiHlP0FVF/tLhAAAgAElEQVRrKlmXGrRCnSeBSkifubk6IXDCp18t55JpOnpbKJwJ3ccgbwL4xtDyaAWxdsg4/eETvpA5Tl7qbTkMyNLZqHZpPHsnrkTXbYWi6dBwELIKob/dVCrdKAILnRmB7hbw50IkF9lfh5UfJn20FmfxAnTtLlJ7Xsa59c8RTZtM1bR4Dgy1gh2CdAL6jiJmfQS8hKmeeknz6qbMa6wX3dOEPvIC+ALo4mpEZAgsBT6/cX0Y0e3KoB81HEOWz4UjzyOmrkCu/BPUtgfhyCsw81po2guBEFqlEcIHvih60gIY6EcP9UL7UUQoAtNWQnIAYVloXxg8D7qboK/F+McKCUhTwZOWGYvPb1wIXrsha32GdEEz5o1aAwEXbAe99x707NuQlm+kMlsCTUfQyT4I5TE5MMQP/uoTfOa7vzprE/2Dw9xx+0fZfM+PEPs3oodbIavMfDayKqF7P9biGyERw315AyIYRlbPhdiLQAiRkYmWPai+AeziQjytwUuhhvqRmRGscATv9m+g7/06biADe96qc96GNftqQ2o7T6FbT6ITw0iA7CxEaRmidBLuqVp060nc3c/j7dpsnBCychGZuYjcYkR+qSGcGTlvSITtG/6UNAJ9eBtixpXok/uQE2agelqM7toZjxg+jnRjqII5qOcexv/hz2HPXUF6473Y19yGVfIyblcHqrcHPdhrftobIJ0yDyw+B7LykIVleOk0KgbYmYhUD/HvfYnwl75L9Hc/InDVTaSfeZDgqtt5bcRqoBevucGklfV2oQZ7L/Qj+M5DeXiJOCkngMzMRmltQlreZB0ZDOPMmEvw9s8jbBu37RTpFzYYa7lzoPF6uhBeGquiEmE5qMF+3NpDUHeE9JFXsUorsIrG4V+0EhnJw1lyzVt6GzoeRUeH3tI6f0g4n775Mt55hEIh/uqv/mr096uvvprx48dz6NChc8juI488wtq1a0e5xqOPPspdd91FcXExAHfeeScPPvjgm5Ld3t5edu3axc6dO9m1axcnT55k6tSpzJ8//4LH/YbsderUqWzfvp1IJEJ2dvblAIncMlDF7/UozoUQxpXA8p+jX9VuCtIxSI3oIqM9kIoai6qxNvX6qeZ3G/lViOxxb7qYKJiObtsDgRx07xhPdsozxNOJIJzTX4QiuwrdtRecDLDD4AuZVzt4QeRfFs9EddjQ+DxMvApO7TRBF+OmQ0+LsYhKD0Gs18S42gH0kfWIymXo2BB2fxQZCZHc/gr+aROwRB+ph3+KM3ci4uQWxMK70J0HYcoqOLwewhYcfw5RaGQM2AG0L2Q+uUpB+VIs20F5LvrY81C3E21pyAyDE4R4DDBNOM64fFKdPQS234OevwaOv4iYtBS54uOoHetg93q47i/h4DMQb0FnhRAyaPyJAw4kPcSM1SBc9N4NxqXitUNm+xHZRVA5B5lZMOIcEEcnh4y+MBU3118yaoiwlCAtU2G1/ea9jXX8E1HoaoSSCRDwwd7foGZ/Amk7iMIqo2kdHkA4IfRQJ3d+8Ws8+fxLPLK79qzNvPDyTr79f77FVz62En1iL2KKRGeWm89LZDL01yJyp+F730dRPa14rzyNyHYh3QHhTGQwgBqOmmAOLcCySG74Lf733YosKMEqrMC76fPoJ3+EK8Gecy7hFVl5iKw8mDT3rL/r4T7U8V3IeC+iYhIsuQF18hCqvxudjEJ7E9QfMcfRTY+knmlwHITjB8c/KgsQysNe85f4bvg0rtaogy8gZl2NOrkfbdkmLMMOgt+GZBSv4QT+hVeTeuJX2DWLcNZ8mtTmB7HGTcQpm4I6dWzU5gxACwkaVG83qrMFXX8QvDQynAWVk1D1LvS1kXzyHkIf+jSxB3+Bf+ES0ruexZ69DBCIcCb2tFnY1bPNrNR7/NCttUZ1tZn447ZmsCUiGEKcJ0FNZkWwyieROn6IwfvvNuQ3nIkz6/zV+EBpOaqvk9SmB/HamxGhML7psyGZxOvvwTu0C/fYftIHdyGLy7GLx5ko6DEgwpnIvAKsvCJEwOxPBMPnXf4y/sBwifnsdnd309DQcM5Mf0tLC7t27eKb3/zm6N9qa2uZNm3a6O/Tpk2jtvbs7+rXsG7dOnbt2sWuXbvo6upi9uzZzJs3j6985SvMmTOHQOA8kfTnwRuS3QceeIDGxkYeeeQRvvSlL6G15oYbTEfxe/0F9Z7AjZnK2qUGrUYqfklTmRgDwnLA8ZvIWsuPsALmVVqv29R7KdPQ0Lof1VWLqLwCYZ9/qkZICx3Kg6EOZNnS01vQ2lTfo53odD861gG+8Oj1KnwhRPEitHLN+UxHIdqKdhPo81YXFSKYh8gwJFwWVaOkBY3Pgg6CL4DIm4CWwuh2LduQzJKpMNwDvc0wq9SQJDeO9Nv4p9eQPHCIwIxKdE8XqUPNOFMK4NQrUL7chE1kFcH/z96bx8lVlfn/73Purb2qq/e9k3SnO+nsW4clBMISZDMguACCiKLC/GYcHfU3OjoOyvD7Oo7OjKN8Z1RUBBQEEQhLCCCQkJ0snX3vJekl3em9u7rWe8/5/XE6CYEOJLIEgc/rVal0911O3TpV9znP83k+n0w/eDLo3t3m+hyh0RxZ1BxchkJAdhmifBpi4rmooX5Y/yuTxafbvCRXY+VHSO5vA68H6p9GT5wPDWsRVXORZ3wMlY7D8/8Di74BrzwGA52QHUWEQoiBLrSbRNU/jSitRV7xT+BmEG9g9aq1RsQHzL6DXZDuQpM218E1Y9KpFLjDaDfDqLqiyoVUAtlzCIrGQc54qP8tasZnEIU1aMuGgV4oHA8dO5EhH7/44kd55cA9tHX3H3eo2+99jAvOmM0ZBZ3oSecjYq3ocAXCE0RLG50eQngjyLxS5CWfxW3ciNr1LCIYwfLZuH0ZtBQIj4XrSLyxFtIrl+CpW4BVUY1VU4fb/3H0iodxOluxLvrMCU0GjpvL4Rys2aYEqwd7ULvWIpwY9tgqZMUkiGQjvMcHUdrJoHs6UN3tqK52U87GaN6mH/x3vJ/9F+zLvkBGK/S2Zcgp56I2L0f4bJSbwfL6cZ04GoHb3Yfngo/jrHka99f78N3yXdSujWR2bsKqmYFdOg4RGpHcy6TR/YfRAz3GtniwF5WKo7s6cNt2IzMxHHxkVjyJNe1MAouuJ/nsn/BOm0Fm5dMj81cfy+qf1u+bEYx8ZwrAzo8i84ohFEWfIMOrk0lSy59Gp9P4xozFO9NoDqf2bIe+12eptdYk1y1Hp5J4xk4mePUtuLs2kV77AjqTNkFr2TijctHbjbNlDc7uAOIEbmha2ki/0V0WwbCRg/Me+Rx+AO/LgD1hKt6pJ5/l+xBvDzKZDN/4xje4+uqrXycB9vjjj1NXV0dFxTHFpHg8fpyqQiQSIR6Pv06zHOCb3/wmJSUl3HzzzXzqU5/6i5rSXo035SWMHTuWr371q3z1q19l3bp1LF68mEAgwG233cbChQu56KKLqKure0uD+GuBCI6uu/hewqiS5lqDShuOpZs0AV6yB5wU6j3FQdaIaAGioBrdsBxyxiEK30A0uvxs2PMkyhnJGmpluKBHeKKW35TQvWEIFh23q5A2eLPMgze/ReienWhPGOGLAiALJpiA9+AaKJ4NAz0mO9nXjS4oRqQGEE4S7QuD7kPv/7NpZJPboLcH6cvgX3ghzisvYc1bBGuXkN57AKu9G/ujXzNZ6aKp0LQcRALsiGmYwjajFXKkQaUEiqZDw0qofwIdCCDyyo0bm3dEnkhaIBTCVXgqSomt3EhgxiSsxg3owipo2ogYOwt5znWoZ/8Hnv854qIvoVc/gI6nEN5cKJyICAbhYD26bQPu/asRl33ZvMb27XC42QQ/RzrIR9YNwuMFn8k+4guYn/WrgnaObDf6u6AzabQ/C9WwA5FIIGpmQ8U82HwvevInTSZ9oAvKq0H6oHMPuWPGc8/XbuCS7/wPr25JcByHz975M9bf9U9E1j+InnczYrgdwmUQrUJ3b4f8aUe/dK2qOej9LyI9IZQlELZEx5PIcBh3OIWomYPVuJnM9iB6eAi7dhbW3Ctw80rRT9+FO3AI69JbTQPRSUJk5WGdecXInGtHte0zjl6j8Tcx8mNWTjbCHzbW1cIiPTxA+t478N5yJ57Lv0Tmyf+FHSvQtsfIbKHQ+WWI1gbEUAci9wxUVxcUT4DegyT+4+/xf/67eMdPxW1rILNx2bESuRDIvGJkfgmiohaZlYv9KpWF1KP/i965EdWfJPHz7xH+/j34zrmY9PqX8S24DJlX9Be7F74b0MkEqrcD3dMBqdEoCcBAD7ZfYp91MVoJUiueRQ8PIoLhUTO7AgieeS5y3ATcg40MP/s4Oj6MXTsX36TpOK+8gNu022S8x9eCkzHmJaMllLRZ6JBOo1IJdH8XbkPcqJZ8wPo2Xg2dSnxwgl0pzOOdOvZJQinFP/7jP+LxePjud7/7ur8vXryYW2+99bjfBYNBhoeHj/4ci8UIBoOjJk//+7//m40bN7J48WJ+8pOfHKUt1NXVMXv2bKLR6Cm8sL9QeiyVSvHcc8+xePFi1q5dy/bt2998p3cY7wUC+Yd469BDLehkDyJvKnQ3oPsOIMadjRhFegtA7VtyrJtf2hDIhWAeWD7Y9SconGLseKOVb8kJTGuF7tqMyJt63HHU9j9C/mQ4sBud6YXORhg3AZGJQV4V2h+Fw3thsA9x3lfQK38JyQRoEBWT0PEhnJZ2BBJSg2iG0cNgnXM1lmoF4YV0H4QLAWUynXqkMW2EO0omYQLh7EoIj4GGNeh0B8gAHNhntnVcCAQgtwQdKSLx3FK0109g1ixkMAsiucixMyFSgHri340W8YxL0St+C2VVCGFDrBcKayCYhWpYBe3NI6oQRirNNKv5TBZSiJEmtJEGNA3YlqEvCGnGK+2RZ3kseH8t3KShUZROhk3LQAvEzIsQhWOhdS16MAndrYi5l6Ib94PXgSmXwNJf8a1HX+bHD7/4ukN+7iPncPfXr0WUVCEmnGM43MFCUwVQLiJcenRb56WfgMiCvRtw+2M4rsRTWEjmwEGsyil4aqej92/CHTsXGYrgmbPADHuoF/27f4ZgFuLca7GqZr1uHG8VWitjhJCIoZPDkBw2BhXJBM7BBhg4hPcL/x/SGyDz0A9RB3aivUE0EnwRVEczOqOhbAoipxDv7Hm4B3bj7t6A7j+Mb9Hn8MxecPw5lYvu6TTGF4Mms6uP2AaPGDK465biJpKow4cQReWEv/kzVGcbmT3bUD2dprkSEFlR7PLKE9IF3g0Iy0IWlp6yOoTOpHA2r0R1mnlgTTKKCoymzas0mb3bjIZuMIL3jPOwispwDrWSWPcyaqAPmZOHp6AAd8sqdGwIWVQGvtFoCRr8fmOxnMmMBMQCUgmz0PwgVlwBe8IMvHXnvmPHfy/EF0cb1G6aSVnWqZXwTxZtg0kuvm/zm75OrTXf/va3aW1t5e67734dpWDjxo3ccsstrFy58rhM7nXXXcc111zDpz71KQAeeeQRHn74YR5++OE3HFcsFmPz5s1s2LCBjRs3sn37dsrKypgzZw7f//73T+q1/UVLQZ/Px6JFi1i0aBHd3R9c15YP8fZDRCogkG8Cy6xKRO44aF6Lzio2vNXXbl992QkpNarsLGjfAPnVaDuAyBoz6nYnNS4hIW8qumcbFMw6xo32hWCw3QRzJZPgUAN4AuAmIJ1BhCy0HQTnMLp9EyK7DC2T0NRggrSScXiES+rAMMIjsLKyUPEm9P560gOHsWfUIbUFydhIcCgxuSLPSCJUG1oCCgaaoW8veAVoDwwPgW1DyjHjcxxQCplbRHDRx3C3riK+fgNWQSn+caUoJ42ctABx6d+jF/8b+sBmxJyPoTc9iS4og5IqxEAbtG5F5lYYNYwRu10d70fEuk3GVmcMVUFgDDMALa0RKa+R0rXOgE6NbK9OrHLiZMDyItt3o6efgT7UArtWolr2IsIaUTgJ3dFosvihLOhtREoXZdncccsneLF+H5v2tRx3yHueW8UN86Zw/mVR6OtARPPQqQFEsAjVvQ2CRcfoPaEcdOshRMAPqQy6cxAZDiNsGy1tkqtfwHfOQqzGetTEBaSXP4l3wSKsSC7q8/+B+sO/ol+8F6d9H9a8T7ytWU0hpMnm+kPHcuKV01B7N2ArhaM16d/8C94v/h/kx/8B9R+fN6oB4Qh6OIa0PLjpFHbdPNSB/bhtzbi9vXgvuQFn8wqSf/w5yUfvRvj8JitvexAeD/iDWMUV2JPrkBOnG/kry9A1Mq/8GfvCT6CX/h6dnY8+3ErqT7/A/4nbsIqPv3mqwX7c1ibc9gNv2zU5ZTgZ3DUvmkWZx4tdOQF7/GRk6I0bnoTHh2fuRYbv27yTzNLfQSA8uhoDYFfU4J17Hnp4iPT6FSSXLcHKKyB04WXIrBzcni4S65bjBvIQ2WUIr2UWta+FBt19GGd4EJKJY0YjUhr+9gcUIufE5lcf4u3H7bffTkNDA/fcc8+o3NnHH3+cj3zkI68zgrjqqqu45557WLDALKLvuecebrzxxjc9XzgcZv78+UybNo1p06axZs0aHn30URoaGt7ZYPfVyM8/+RLdh/gQJwNhB6BwDvTvM5Jc489F73sR8qtfxzF+I+64zK9FdWw2SgHpfnQ6G+F9vbXuSY/L8kK0Gnp3Q96IgHl0LLRvNY11ttfwdof7IZxlJLosbZQV8kqhcSWc+UVY91vILUL3diLKx4Pr4r/kKtIvPEa6qQVvbRW6sxOZU4azZR0iUozMzUcGBYRDr+LngQjkoIP5JgM6cBCGu4yKg8cBNWQy3uk04Jhg13XRWIhwNtas8wgH1pJJQ2zDdgIT49jKRc68HC6+Df3c/6AjBciFt6HWPgLNe2DGJYjyYfShXdDbYoJZXxgRyjXX5Oj78+r3RSNc51g2ejS5sRO9jakYunErurAO0d8J+XkQCiGSKfSBZvSEyxFodCKNyAqjWxOQHoK5H8W75QXu/3+vo+7v/ovEa+TIvvKbJ9hw9bV4G1fDzGsgPYD2hhHR8TDQADmGPiNyx6Aa9hqr3IEYwgIViwHgu/IWMiufIvX84/jOvwK5fxV6+iWklv4B7yXXIn0BxPX/gvvU/4Vdq3E7miCnCLx+pDcI4WwIZSPCuZBT/LaVoeUEQyuzlIvb4pqA9/N3IvwhtAMilQDtogvKEC0NqNVL8H/p+6SWPIi37hzU4Q6UsvDd9C2EdNGHDqA7D6Liw+hkAp1I4OzbQXrTGpMJ93iQWVFEKIxVWILOaDxnLYRNq3CSw6TXPIs9uQ578vF0N5mVjZz89me8/1LodBqnaQ+p5UvQ8WFTcRgNroNn8izsSTMRUprMbuUUo4jgjJ7ZVc07yWxdBcEI3hnzkLlFuN2dpFY+jxrqx66oInTRFQhfAHewn9TWDYbPPgpETjGvy0M7GZNZ/oBCVtS8+UbvF5zmBrW2tjYeeughvF4v8+fPP/r773//+1x55ZWkUimeeeYZfvazn71u3+uuu46WlhYWLVoEwCc+8Qmuu+66E56rs7OTjRs3HqfC4PF4mDZtGp/5zGeYO3fuyb+0v4TG8F7Ee6HM8CHefuhUP7pvH/gKEPF+RNmpKYKoTAK2PQDFUyGUb7LFJ+G49oZjirUZY4DIGJSTga2/h+rLYf8GdNsGEBoqqhHpQQgUoXOKTZNaRyNMWQgNm6BsHGxZB7kFhtMZzkNUn49adi+p5iYECTwzzkY17gMLtPRBIo5OxNHOqwI3jxcRzUNkhbEiWcjsAiiohIan0AODMJyBwW4jQQZQUQOeCBSONYnWZAJ2rUSHshmub8I/bTp2SRFyzsfQjRvQ9c8g666Cqjno5s3o7S8g8scg6q46am5xZMGh44PQfQDd3WxK1SM8UuEPgy+M9voN5eHIPhrMPyf+CtIa9NZH4NABmH45ItaBToxUk3o7wQpBvA/yxsG4CbBhOWLuRYi8sagn/gcqqvjF75/kb3/+2OuO/eMbP8I//NsPoG03zLwKke5HhMvQfXshXIbwhFC9B3CX3YMMhtFNTaS7etC+CJbPA7kVeC//LM6u9WRe+BOesy9EDnXDnCtwtq3Dd8VnjDRbOon7/L3GyCE1DJnUSJZ9RFkBo+5AdjEiuwhRUo0orUEERqfunCzU3g24LftwWxoQOoOcehbuCw8gQlG08KCxUF2taMfolHpv+hbu2j+jUwk853+UTP06VEfL8ba5WhudWK2wCouQkRC6s5VMSyPEjfyVZ9pss3DoO4Qbi+Ps3QIeH3blRMPZ9njB40OGo4js/L9YT/htgbSQ+SWGgxw6ueutlUtmRz2ZnfWIQBDfmedjFb25ggyAHh7E2boa3XcYkVuEPX0eIhjBOdhAetNqdDp1zGntPWS88UHHeyG+OEpj+Oysd5bGcG/9eyKOWrhwIW1tbQSDQWbOnMncuXOpq6tj+vTpeL2nTkl8Vxnt/f39fOc732HVqlXk5OTwta997WiEPxrS6TRXXnkl8Xicl19++V0c6Yd4r0D4sqFwNrprMzrWY5QRTiFYlZ4AKn8iDLYZlQT7MITemnycCJehe3ehk31Ifw7KG0IkutHpBBSOg5ZdhiPrj0CyHzEEOp2BaCE0rYHqi6B1LYytgabdkF8CIxQAefYn8PsfwU3ESL38It7zr0TKpNEL9gRNY92RTnatcHtaoWUbbtsBHG2hMw4kY9iVOciwH/xe6O8ybmsZBxKDiAV/g17zADq/FBEIoacsQOxYTmhmFcPbtuOz/XjrnzQUhq5m1I5l0LYLOeFsxKVfRm14Ar30Z4iqOZCMoYa6TTYbIJAF4RxDWRjsgt52k+1yM0YbWJngTsNINvdNMgnpOEycZwLanS/B+DpEpADduhUsINmPtryI/nakZyYqkIXuaTULiOq5MDzAFy+dzW+fW8P6xo7jDn3Hoy9z3d8PURLOgn0roHIWOjMM0fHo3h2I/OmQVQIBP0oIhBRIW+Jm0ljFhajsPDJL78ez8FpkIERq6QPYE6Zi1T+DZ9qFpB7/Db4rP4vw+rEu/ixq3ZOI8EglLBBB5JdBbomZl2170I2b0a270fs3mux3MGIC4NIaZMVkyC46JRWcIxlelIvbvAfd32PMQhwH/H4YHkbaHuSc83C2rCX5o78hcMvt6EyG1J9+je/Sa5FzR+dBqtggzv6dZJr3gqMRxROwqyaawH/LRrxTZyJq56LqX8aqnYm7azPOvh0Iy0aEw8iCIjQOerj39CoyKIWTSaMTSUOZkdLIeAWzIBAcdReZW4g9dyHeaXWoeIz0uuUklz2NzC08If3BrpyIVVKBCGXhOdsoGqmeQ2TWPQ+JGLKimsAV14Jl4+zZSmLx/Zyg3fj4Hy0bmZuPlVuIiJxaw877CTKag4y+uZPm+wLvkQa1dxo33ngjdXV1TJ48+YT29KeCdzXYveOOO/B4PKxatYpdu3Zx6623UltbS03N6CWIX//61+Tl5RGPn6Ar9l2GTg+am/Z7DUKM8Dlt8yw97ytpOCEtw+PFdNtTfGoOaHLMfNTme0ypLzMEbo7RJX4ryKlFd9Ubd69AFN130NjkhvPh4A7wBUzTVrLdBJmhXCAFrd0INWQCj6mT0U170ANdCF8WdO1DFNWix56F1VmPf95k0js3oHQQIfebZrTjAgNtGvG8YQhHkZaLDEis/CLcXcuQORFIuybQPXKPzKRh7SOIsz6NXvcHdE4Bwh9ETzkXsf1lQmdMIb5xC0LOwbNlCeLs69C7l8Pu1ah0AvwR5OQFMPEc1NbnIBhFjpsFBeMQoWzTKNV9wATj/tBIdjdsdHj/gjmp0wnUkz+C82+G9fehY/2IZAxRNg2990UoKYWmVjRpdHQMZO+Cnk6oCcHEWfDUL7Eqa/jpLVcw759/zavrWIPxJN/6+le4d+kL0LgG3deFiCQgMhYRKEAPdyBDxahQGN0zCFIg/F6Ea6OsENZQO25+FZkXH8Fz7pX4Lr2ezLLH0AUlWLtWYI+ZQPKxX+Nf9FlEIIh17iePva74ILqnDRq3GLtf7SKq5yKrZoC00AOH0Q316Lbd6K0v4m5YYsrq/ggirxTKJ4/u9OXxIcYeU5Q4EvCqVBJ322rscA5uPA7aQXi96GApqv4lfN/8Fen7fkTi/34Lz4WfwP+xz5F64TFDARIcU9cIhBGRKDK/GE/tjKOyW0coACqRQeQWkN6zHTsew548B2ffduRZF6FjA6iBPlRvF87ePeA4R5Y9pw8ChCWRoSBWSSlWURHC9pgFlB7dqEe17yN59wrQIAvLsaacgXf2WaD06FQCbRrUki8vRebk4ztzATKai8wrwXvBNYb329pA5qU/gXKxamYQ+NhNJ8Xv1pk0qq8b1duF29n+Vq/GXzU+MMHuBwQ333zz23q8dy3YjcfjPPfcczz55JOEQiHq6uq48MILWbx4Md/4xjdet31LSwtPPPEE3/rWt0aVtTgtsPwjVqjvNegRC9akKVcrZ0QzdhS8KiB+9fNbLe2/44hUQHIzuq8LimpPPXCqvBiaXwA5EW0FTlGO0ljgiuCxjLAQAvKmQf8eyKmC5rWIMedBxz60sGCwF503xujQZtLgSUI8Ycr5LZugcAJ64DBMmgPb18DYWnRPExRORJbWojp3ILJK8VUJdFYFIr/aKFL4wuANGZ1hrSE1BLFDqHivoTgMDuEOxFDdA6ixZchM3NAN3JH54LhGsWDtHxBnXYde/0e0chHBLHTpeERnM8GzphPfuA2tp+H1LcOafCG6+izUK49CXxtqy1IIZCFnXALDfeiuA9DZiEKbhYnHf0wHWLmmVK/ckRl5Ai3jEyE1DBPmw5o/wvQrYPsT6JmfQnQ1miyck0FPPQfWPG5ULvILYVcb9DQho4Wo3GK0L5e5E8q55bxp/Gr5tuMO/7sVW/jiw79n/oIZMHAIHZ6MSHQhQiWorq0QLEAEgqjhQ161b4gAACAASURBVKasbFkIBO6hVuwb/gHrxftxiyeTWf009tyFeOZdhrN1Fc7gALYdwJMdJLn4N/iuuBEZyT42f4JZJntYYRZuWit02z7UqkdN9WLsVOSsjyDmmCygTifQ7fvQrbvQvR2waQmjMtDSSQhnIz/x7aPZEDmhDjuTIr1tNZz3aXj+HoSIouyR61s6nsy/3YL3pu/gdrSSWfo7nJ2v4P/C95GvKhdqrSEeQw0NoLoPkVr+JKSSRx3VrNJxBD56PYknfw/DQ7gd7WB5saJZqLTCU3ehoQqEsgyVAaMLTPo0apc7GdwDu8ls30i66SB61z6zoLQkwjP67VF6bKyKcjylY9BOBnfFH3GREC0yluGvhQbhC+KbPA1RNIbUmhdRg/3YZWPxzpmP8AeMTnNFNdp1cfdvIfP8H07Mn9T62N+8fmR2ATI7Hyuvig+qzu7JUlDeH5DHvl/fiWO/T/GWgt3LLruM5ubmk/Imbm5uRkpJZWXl0d/V1tayfv36Ube/8847+drXvnbKLhnvKJL94CRO9yheDyFNI5IVAF/kaGf0a6G1HpGtyowExymjuatHfNbfC5AeCBS+LpgVQoInjM62obcJ8qpO7bDRcpQ3AvE+0wB3io1qWvog3ol4lV6vsDworRE5Y9F6JTqcgx7qg2DI8Em9IdOENNyDSIJ2FJSOgcYdUDoT9q9FTDwDbfnRiT5EYY1pdiubATM+But+A34vIpCD8EchHUMPHYLk4Ag1wAFvAJFXhSw/EyGlMWeIdRLf8Cy6f9B8wj1eU6IFSCXRwQKE7INV9yPm3Yje9KgJeAvGogd6EH0tBGfXktiyC+06+JSLmHQB1vwb0N0HUJufgcEu9Nbn0ZbHZBxdBxIDaNcxmVwpTJDvjDyENHxdyxqFovsG2b1MGuL9EIwi+rrQOVWw+RGYfjW0bYHEMDI6jJIWavuLyKqJaCeDSsWR/nyouwheeAgxpoo7rz2PRzfupzd2/Gf4y3f8iA0vPIJVVg1NG9BVs4wcWbQKBpoMdcRV4PMhEglIJo0aQyKDmP8prFV/RJXPxNm0DHvq2djzLkPtWEdmzxasimo8kQCpx3+F77IbEXmjUxGEkIjyiVA+0ch7NW9DvfzQsYY/24MIZyNKJiCrz4BQ1HT/v8bUQ6eTuIv/E3X/P8H1dyBHOvTl5LMRK59C7Vht5kkihvQGUEJAtADrrMvI3HcncvJZ+L/yY5K/+B6Jf7sVWVSOLChFllVhjZsEBeVYoQhWcTmeqXOPP2/7AdLPP0Lg8uuIP/E7VOd+aG9CzDoXmYpBOAvtOOiOgxAfMlqy7ujZ03cVWuOZOB3vpJmInEJEXiEqPowaxSACwD2wn/Tm1aR37QNpYVVU4Z1ZhxjuRYxi266VQh/ej9u+Cx3OR+aU4q07G2X7STz7J2M2MWGq4enaNvbE2TBx9skNPZlAD3Qbt73ezrd0Gf6aIQrLsIIfpID3Q5wq3lKw+/Wvf52hoZPz447H40Qix0/GSCRynMDwETz//PM4jsPFF1/MunXr3soQ31ZorUaXgzndUI4JwlU3uGnjDjYKhLTB9pvS+5FnX/g9RXvQmRjEDqJDpYjXZtGjVdC9Hd21H3GKwS6AqFqI3v0YumDqCXV7Rx+Ugt696OzxED98nLmIkCMfIV8I+g8ihEAXVMLBbebmbnnNtVUWhP1GRSAQhfYtIKUpq9fOhh2rIHQAnZUHThppe1HjzzUc3+6t6O7dIw1h4tizEBDvRw8cArkWHc5H5FUaM45oDiTTkBMCkT6mZ6s1tO1DF41BRD2w6reIcz6L3vgY2uOHmtmwfSVi+DDBGVXEt+6DYA6+5BNQOtm4p134JdSelejO/UZmzeNHRPIQeWMgu+T1rnxamYxjKmaas45og76as3uC+afjA+hXHgNvMXrfGlh4G6z6NXrP8yBstJNCJPuhoNQ0/k07DwrHQHcXhJuRwQjKstDhIvJzs/nXa+bxt/e9cNw5tja18r8//il/e+t1yPHz0G3boAzTgDiUNg19aEQwhBhOgBPDqig0jV9eH1bdFciNz6DHzsHZtQFrTA1W3UJk41ac/dtwpIVdO5v0U79B+8KIYORoZnPkAiGkhRxTg1012VjVVs2EqmPNmDqTNuYSw/3ogS50+34jR3fEOvgI3Azy0tvQq/6Iuvcf4brbkZFchJDYtXNIr1qCXVQKXR3orDwEEqEz6P3b8HzpB2R+9wPSd/8zvq/8BOfPD+Ee3IvavwN2bQK0CboDIbBs7ElzsedfgfT6EV4/9riJyJwC0i89QXDRp4k/fh/qcBPuxpfwXn4jtO9Hjxg1CGkhi0oQhRWI0Gnkmlq2eT8A7bro/i5U9yHEUB/yBIkNWViAvfCjALh9PTjbN5JY/ID54wnmsQiEsCfPwsokoGUbTut2dLQEO68Ea+581NAgicfvBwGe6WdgV08+qe9k4Q8g/BXIooo33fZDvE/wAeHsvt14S8HuwoULT3rbYDBIbESy5whisRih0PG6hPF4nB/96Ef88pe/fCtDe0cgpDSl//cclMk8CY2WEuTonYpaucYi14kfc7FSLmg1ooN6RMP1dEJDIA+GWiBQgPAeC0qFtMHyoCMF6IE2RPTkOqCP7u/LQkcrof0V9CnRUTR4QtDfgM6uOj7g9edDottwdXsaEYXj0LEOk6xMJ4xznT8LUglEMolOulBSBvt2QkkVHNqHKB6P9mWhew8gimugZT1UnoMsrEV17QYFZGWP3Ej1MSoK0mjVpmKQjkG8FT14EBpX4xlTiurvBq8XZNK811Ka58q50LoFHclB5BXByt/A2TeZ59p55rFzFXiGCc6aQHzzRsT4iXgLEuiNjyFqF2BNOg8mnXdy111Iw2f2jd7w84b7Zpfgjp8LTZuhfCq8+CvEwi+iX/gpBKKm5KxcRG4ZengQvXsjlI+B+pWogkJkQQ3MvAC2r0JU1PCF8+P8evVeNu0/Xnv39geW8Ml5Eyn0FSKDOejB3pEgFwgXQVYIbdsIIRBSgwu2NYg75JJub8YzaT5i72rkmDpUdweiqw05sQ7LslH9vTjr/4x16S1IqSDWgzjiiKY1hKKIsgm4w3HSK5egUwmQ0pS2yyoRkWyjYpBdiMh+YxdHnUmhXvwd8rzrUduWoX7/XfSir2CVTcCasQDWPgsTzkQf/hNCK/TwIAgLUZaPu/MVPBffiNu8ncy/fwH7mr/Ff9Ut5rjpFG7Lftw9m1BtjajhQTIbXiJTv8y4uI2fjmfBVchoLlb1FNy2RvwXX0Pyqd/hDh4m88zvsMdPRhSPM49w1GR5u9tRLXtOeV68bXAdcw0AEcpCVkzEqp6GmPjmkmhaa3RvJ2ryHNyOFmOwMVqmWimcpt1kNq8mowUyrxBrbBXyUAPq0D5UwyZE/ljswnKsKWfiNu8n8affnkD6TB9HYxDhLKy8QuQHvEFNhMLIwOgaxx/iQ8C7yNkdN24cruvS3NzMuHHjANi9ezfV1dXHbXfgwAHa2tq44YYbAOO9PDQ0xDnnnMNDDz10WuUw/lrtgo/A0Bgc0+jkpk0g5mZGntOgMqPzAN9NuGkTkCUH0QMHTZk8bwrSP8J3jI4HtQ/dsfOUg10AUX4GumWdKamfLLSCTAySfdDXgM4ZD4kuRKAA/LnQtxuyx0H3y1CzELGpEe0LQlsjVE42DWKJAZAR8CQQWqCziwyFpL8LXVoNYydC03b0jheg+gxIDiL8WYixZ6PbtsKgY7KiRxokj8h2aQfKqxEhZW60zjDEe5GuDzUgUENxw8ISGIMJ14V9K6HqbOjaj86kEEVjEavuQdd9HDY8ApUzoXwCtDeAP4vAnBqGNzcgUgN45n4E3bQRLW3ExHPNtRk8jO4/BEPdRwbGqDNRCGOpfHSO6eOzkqO+YQIx4Wx070HT+JZTht6xDKrOgaZVJkOaSRnVglYFwSzo7YJwLiQzMNSJLChBxQfRWdOxbIv/vv5czv3XB447TX9/P//83G5+WTMV1SMQER/a5zfjzS5B5uag2zuMmoDXRvmiWKkU9uyLkLtfIb18CXZRLrJpHUxZiO5swdm6Grt2NkJtx7r8Zpzn7jPmYcJCB6LoUB4Eo+hDfbDhFSPdJgQiKw8i+TjrV6Jfeto0PSkXhET4/Cbz6/MhI9mInAJkTgGEIsYl2/YgF1yPWvYA8vzrUdEC9FM/xT3raqwZF2GNn4La8JKRRBvoQfiDWPOvQu3dBB0HUEVjEb4w1k3/jPPAD3EW/8LMG18QUViBHFuL9+JPIsonoLvayCx/HLejBWfbWtwdaxF5JQQ+9x2ST/0e77iJ+M5fRPLZh3BRqO5+xOGNsGMj0mMjtGO0hU+nGYIQCH8QkVuMyCtG9XfB3k0mMXBCXqRGjpmEHDfZWCfnFb/pjVSnkjjb1+FsW4PTsAtn01q0149n0kxE0I9o3goNG3E3vYgom4CnrBL8oy8ORSgLmZ0P0TzIZFA9h1G9XaiDDW/pUvw1wyofhxx/ao3Lf7U4zTq7f6044We0tvbkm4BOhrMbDAa5+OKL+elPf8qdd97Jrl27eOGFF/jDH/5w3HY1NTUsW7bs6M/19fXccccdPPbYY+Tmnt5uSz3Ugk6fHG3jdEBIj7HOtXyveniPltqFECA8hhfrGX0VfLqnutYKBhrRUiCK56CdFPTsQAkbWXomwvajURDIRse6j0k5nSSE7UdULnjzDV87rkOb0ckeiPeCwGR4E92IQD5KK0SkFC1GONFIKBoHrbsBjXY9RktU26AUeqAXikphzybIyoKhPkQ0B105E5rq0a17QGUQExYiwkWQXYR2EsAo1AvLD93GulWUT0VX1CGSvbD9YbTSOG2H8RbnGhoDyjSo5ZVC4xoYV4cYOozuOWSybfVPoivnQl8bIrcI3d8Jve2IyQsJTZfEVq1HhPxY4Sii+gz0tqVgexFZRZBTZmyau5vRAx1HFUKEN2gUBPxhswA4SnHQx57eSILMSaM3L0HMvAK94QnD3+1vg7OvgwOrzFxODENowBwuko3ojqGH+2H7KpTPRlbNh+rp0HEQKqdwttLcfNl8fvvMyuNO9Zv7HuSWq+Zz9rmLUJueQ6RiUDnNqEqEQujYEMIXRFoWTk878opbUGsfwzr/RgJnLCK5+B504jDWuodhxqWmeam9GZlVCC278f0/PwZfEN3fiWrchm7da1Q8lAt+iZxxnrFC7j6IPrATnBDIiqM3IK1cdCKJisdRyQTEulG97TjJlKEzWLbRNw7nELjiWtTyPyAvuAEdzEI9ezeutLAnzCa1px5rXDUc2AMVE3BXPo732q/jHtiF89IfsS/5DO621XhvuQNZWI461Ii7exOqaTvuK8/hrlxsMupZuVizLsB7zd+gY31klj2Ou3U1yafvw3fJ9ST/dDf+T96K6u8ls+ElpMdjVFDSKdx4wszFkebF04YRkxMhd4HHQtgW0vZAMHxineNwNrqvE6d5h5ELLK5E1sxCnCA4BRA+P545C/DMWYAa6sdZ/yLpHetw99SjUmlEOIrvgkWIzBBq52qcxhHayAnGrIWp7GjpGXExtN7XJeg3g8gMwwcl2P0QfxFOGOzed999R//f0tLCf/3Xf/Hxj3+cGTNmoLVm69atPProo3z1q1896ZPdfvvtfPvb32bevHlkZ2fzve99j5qaGjZs2MAXv/hF6uvrsW2bgoJj1n/RaBQp5XG/O10QkYrTHgyeCMeytqmRzG0S0gPgplAnuJkYWsBrgmPp4bSGvNJGZFcbHnL/fvM6CmZAx3q0kzTBanQ8Wh5EH9qKqLnwXRmWKJkJrevNzSVmMpg6Oh6SfaZML2zwR9A9DRAIG9c0ywdtzVAy1vwc70MIPzrRj9BpdG6ZadjqaIDxs0y2rXo27K8Hrw9V1I6MliKKpo+uuKm1cQvz+NHZUfRgI7zyCjpUaHiglm3ul7ZRESDjmOfmrTBuBjSvR1fMQnj86K4WRCCAiA+ZrHphJVTXwbZl0LwO6c8ifNWniT1yP6EL5yO3LjHubU4K3bHPBGSRHKNNGgkbtSoh0bjg9KGHeqFvxETh1Yvoo/8/AdcxkwQZgH2rEWOmoFt2mqz8yt8ZnV0btJNBZJJobxBxcDPWBV/CbV6JXvc8enc9qrUFXVwOe+uRs89H76nn/3zsDB5bsYmB2PGyhrd++z/ZsP5avAuuRy2/H6E2ISaciRQWCgxVyLIhPoAIRZFzLketeAi54Hr8n7yN9EtPkOlswt68FFFWgz7cA1POQvV2olc+ftQoQERykDPON1q7oSg6FUdtfB73oR+a4Cu/HOusK5CFo1tca61NRjsZg+SwkXxLxFDdraTqXyHx5IN4Js/EfukB5EU3Ii68Gb3sPqybfoD18mNoO2wu+VAvpFNknrsfa8Z5eG/6Dunf/xA5fT7q4F7cbavNCT1e7HOuQpaMReQUog414ry8GHfN07jLHoFQFGvaPORHrifz7O9R0+fhPf9K0n9+FP8ln0R4PDhtB1CxAXQqgbACyPxsRCTrtJsnyLxiCITQXe2otkZjxdvVB6pr9B0yKaStkcEgomQcKj6Au2utMVAZtWJkZPhEIIIoKEMWlOO54Gq8F16De7iNzIoncRp2kFr6B7SrkTkF+D96IzKvaJRjAaEwDPWhOw9AVwu6/7CZCx9gCN8HyIBDihO7+70dx36f4qQc1G644QZuuukmLrnkkuN+v3TpUu69914efPDBd2yAJ4v3gsPJXxv0EUWGowFyyig1nC7tSw3aTSB8ucbBSgijLtC/F52JAxJZPAcAdbgehpOI4smIwPFcNa0ciHUZx7RToSu82fC0hpa1aKkhbo5PVhlCGqkt3bMX+g4jys9Ct+9E9zfD4QMwdS54chCDTeArQg+1QiQXskph60sQDEDpBES4DLraTMk+mUGE/Yh5t5xS86B2M+iOndC+CtUfw+nowi6MIgYGIZUy+r/KMRnA8moY6DXqDwLo3ofw+tDVF8D2JVB7HiQHYNdqCEbBslFaMLxxH6Fz5iLzC0ELSMURSLQA4QmgVcYcT/oQUpjMneOglTMS0p7C60nFYOAQFIwFbEjGoa8D0NDdaKTGcBF5ReiBJMT6sK6+HT18GLXzZYgNQMV4hIyglt0PNXWI9BC0NXLXlm6++p+/fd057/jGrXz3Rz9Ha4V66oeIeVdC4wacbVuRfh96OIaKJxBTL8Vz4TXQfRC1dz1y3scRQuA07Sa9/AnsRBuiZhbuvh14LroO1WyaDDXaNOzFhwx1IZ1AhKJYM85DTqwz2+yvx9n0Z0gc3+eAtI1+s8dn3PNsr6FaWLYpyUsbyseTefExVM4YSCXw5fiwLv087q++irz+e2TWLsHduBzbp1GOxvOZ7+I+ew/aG0aWVmHN/xjO4/+Ljg9iTT/XZP2DEdPx33HAyP8pF5Ffgj35DFR8CGflYlTDNhNwF1VD10EC3/xfnPXLTZl/wrRj76mTwe1oxW1twu1oNXPxNEFrhR4aQPd3Gd3frCh2SQX2tDMMPWQ0KEWmfiXugb2QHETaApmbb1RHRrudamXWdNnFiMIKU90Z7DUUIEB4fJBXik7EyWxeidvZhhoYPPHXsFZopGmuQ4BtI4JhZCT6DkpSvbdhT5yO/yMff8eO/16IL446qH3pTMqi74zrYNtAgot/ue59GUedFGd327Ztr+PWgqEc7Nix420f1Id4dyDkSKPTCSgNpwMC0IkedM928EUhXI7Im4I+XA/DXWitTZNQ1li0pxvdsgEKamCwA502yh5C2hDKRXfuelszv0IIdMVZcHAVhEogdgjcFLpsPmKgAbIqoGMf5JTA3tWIvDJ010Ho6YAsBd4gJDqMi9VAj3Evyy0GF+hsMI5r/rChCGxYgs7Og61PImZcefJjtDyIshmo9lXIkB8R8KN6BrEwGVURCqErzoRtS6G9EcproWUj5JSCJ2iMU/Ysh8kXwP71UDYeLroVdi83Lmr+CKEpRQy/+GfCV30KYQvTHJccQihlzCPChSb4ineZjJNyQDkI7XLKOruZpJFw62qFwhJDKcgrhbZ9pnEvnTTBXyZlKCE9rUZGLVQIefnQshcxphpRMRkW3QaP/ATmXQqN27nt7PHcV13xuma1O3/6Gz7x+a8wadIkU/nIpACJyMqG5DDCG0CkhhFeSeaFP+K56JPIVAK96RnEnMuxK2uxSseRXPIAYtt6rLMWklnxOFZhMTKaA7YHItmI/BzABFwqmcbdshxn5WLwBbBmnY/9ya8jX6tskU5CrB8d7zf0jeQwOhmDVNwEUUM9qE0H8V1+A5kX/ojKqSB5qBPPwz/BGjMVtewBPLM+gtq4DEpqoGEbxGN4Pv0dnGd/i2rahu5qw174aXR/F+6+eti9Hp0YPtp8JYSEYAQ91ENq20rzeYwW4LniczgrnkDEutBakLrnB/i/8C8kH/01snQsMmzk/oTtwS6vxC6v5L0EFY/hHmrFadxFcukjxkp7FOhkAiyJZ9JMZM4MVGsjzlAMmZuPOAH/WKfiiKF+dH8nIhBGFlYYObhoATqdRHe3o51WvONrUWPHozpbjaX06w6kjaJFPGYkzUaailUmg+5rf19zLt8IqjfvdA/h3cOHnN2/CCcV7NbU1HDXXXdx5513HlVPGB4e5q677jqh+9mH+BB/KUQgDxHIQyf70D07wBuByBhIDqAHmhDZVQh/LnqwGXLHmYxK8WSk7zVWnUKiu/YhCt6+OSqEgDHz0M0rIKsM+vYjVMZkLbMK0VJAethozWaPhcg+ONyOyCtF+wsQyT2QUw2dO8FJwoTzYNWDRqYs3o/IKoZDB2Da2bB5NbqoBNX4CqJ4otFStr0n5ayEtgEH4bFRHYex8rOMsUM6iSirRUcKYeVvoWU3VM2GrmYonwGH90CmD/ashEgWxGKIju1QUIquPgN2LEMOdBCcVEHskfsQ4eAxhRLXNY8jms1HJMakHJFLe7UY+qvTVm/w5a0UpIYR/mzs3MN4plQjtW1kuTY+DShDIUkMQ1YA0OjuZkTheGTuOFT2fnR/H8K7Dau8Djc7D91yAFFYjj0c4xf/8necdfM/4b7KmS6dznDrLZ9j2crVkFsGvW3gz0IUFKAa+pEeHyiw/B6UP0Tm+YfwXHytsU7evhw5dQHC5ydw9efJbJ9G5ul7sCfWwpgZqOFBE7AeUWPA6LDipLAqJyMso5CgNizFefEhYzxhjfI1PdKRLwIhRCQHkVeMyMlHjJmM2LMOZ91SPJfciLPsj+hICGV5UQMJPAN74KN/h6yowu3uQlgC5/l78d32H3gW3Ya7bQXuphfJPPkLrJnn41nwccM3f/WpM2l0dxuqoxnV3QHpJHqoF2fFYnRiCBEfRNSeidq6hsy65/Ffdh3Jx3+LZ9Y5yLJxyKycN5+/pwEyGEaOr8UzvhYuvvoNt1VDA6Reeor0mhfBsrCKStBDMZQ9uoOasCyQIYQ3CwY6cQ+tQu6tRxSVG87vpLOQpScvp6gzafRgD3qgGwa60YM9KCfD6e+6OD2QY2tP9xA+xHscJxXs/uAHP+DLX/4y8+fPp7KyEiEETU1N5OXlcdddd73TY/wQH1AIfw7Cn4NODRgpMq+R/yLb3BREqMTcSMKlo+9fNAm153mIliO8b1/ZRwgJY+ejm182ChbJPkAYjm4oG314N2Lqheh1j0JBOQx2moYpBxMExzvAF0APDyMCh9H5heBIRHeryVzmVyAcBz2mCnp6YLAdnRw0Wc5M0tA0tGse0gul0xCltcfr2/qioNKIoB+dSKP9fsSwY2gHWx5FTP8Y+iNfged/Bg0bTMDbvBHO+jRseBi8GuHJRQ8cgOBEyM5BdO6AkrHocbOxmjYTKakBNEK5RjXDG0DY5hoghAno0gkz3iPmEsrhdcGtGjE6Ga1sm0mAE0erFI6bR/KVXWg3iU4N45UpvFlh8GnT7IQL0kY3bYTC8RAdC2UVsGszKhpFao246HPoJT9Hl49HdLYwq6KYf/jYefz40WXHnXbFmnXcfffdfHHRAnTDckTZVEN/cB3Dv1QuKi8LsW8bIn8C6WcfxHvJ9eidK1C7ViEnnQOAZ+pcrHETSf7mTmTTHmR+MSISHdHa9Rsagi8I+WWooUFUexN4/ViX3YonEjHKE3nlxkjiNTiqC9t5EN3RhOreDoO9iGg+IhDEXbsE+8LrEGuexBkcxD3UghWxEY2bsSqn4h5YjAyFIDFM+u5vYt98B9a0c5HlE3Be+D3u5pdQW16GQAgRygavH2FZiOwCxJha7FkXHBuL1jDYS3rJPSiE0WqesYDM0t9hT5yF/+rP4bY2ktm0Ej3Uf3Q/mVNweiWzpIVVMR6ZfWqZQRmJErjyBuAG3K4OUquew2lrOgGNQaOTcWQ0G5GVjYwUI8dMAQlq7wbc/TsRa59F5BdjTZmHnDDH8O3fAMLjReSVQF7JKY37Q7wPIOQ7x9l9H9NgToqzC6CUYsWKFTQ3N6O1prKyknPPPfeoJeXpxnuBU/Mh3jnovr1o4TFNa8V1SH+O0bnseAXhyx59p6yx5kbTuBI58eK3f0xuBr3991A4BREpBduP7t0Pnc3IWdeiG9ajM8PohjWAQlRPR/siiN4GCJeguxuhtAbCY2DtI1BQCT4LkV8K3QOQX4hu3Y8Y6jfNX/4soy0byoFQDsIXQg91wqEdMNQDniCMOxNRVI1uq4f21eiMS3LtNjzl+VhOCnwhRHmt0Qaesgg8IfTS/zSSZmUTYGjA0Bae/Q8oqIbCsdDXBo5CTLoQEcpCt2/G3Klt8HjAEoj08FEXPsOTFoa7exIwZie+Ub9odSaJ3vaS4aiqBORWIQsm4PbsJ/38E6QHEnhrKvGW5yHzC9BdveBqrEX/ZPbv2oVa9wxUzzGBVdFk3Ae/g44WQ28LwhsmHi1g1udvp6H9+IakrKwsdmzdTPHu/5+9946u6zqvfX/f2vt0HPQOAiQA9i52kepdtYa3CAAAIABJREFUtootW3LcbdlOs52X3Iy8l/HSXm7yktznxM9+Ltd2HMeWJdkqtgpVKYkSSbF3ggUsAAkCIEj0jtP2/t4f64gUJZCmfK0SCXMMDILn7LLOPudgz/Wt+c35K8ziD6MtO/B2bsPkFqPD3WhOBe5tX0Ffvg8/rwSvvZ3gdbdD/xD0nUGW33E2hltV8Y7sxW8/ht/VgQ4P2ImL51mynxqzE7do3CajOS4qxlZVR/rQkT6krA4Jx6xWNxrHmTLLNoy9LqBCfZ/0Uz+GsUFMbq6dDy27Bd39IqmjR/ESKcJlYdw7/ozET/4WEy9Ahjph2jK0cSvuJ/4Cp3oG6mXwtz+HDvdZ/+KRAaupD0asBZrjZJ04inBmLkFKa6zMJ5Ug+ZO/t9XroR78aAmSSRD+8++86V6hqmhfFzry7jncaCaNd7IJHegBQHILcOtnYyqn/E4b5/yxEdJ7tpLatQEd6MXkF2JKKqxDRUEJJuTiNzfAYLctzjruucCaNyIcsVZpJZMwlfVQMsk6SEzgbcN7gV+c1ez+4Uqq8t8mzW7/GDf+YNP7kkddss+uMYarr76aq69+67ZNE5jA/zLiNTB0EkJx6NoH1VdbSUHZYsZNtfMzaO8hTOllUFhrfXnLZ/9OhyROAHVjMHQKLZmHDLUgsVI0c9BuULcEffVBKKmCtiPWtk5CtpKXTlhXgcQwUpSLFhZBvBI696ORGFJUAkMjSDQEtTchsRJruzUyYK3LTjXb5r3Xli2dQjQ1huxdje+noXy69V01ginKRweGIJLVaHtpiOTDwadh1s2YD/8F/nPfgvbDUFkLmx+Cm/4Mnv0GqEEW3ASj3WjjGnRsGKlbDCVTkf6TaDAK4VwUQZKD6HAv/sAZS9wCEdvYFs5FgjGbtmaMrdamRu2/6WzlN3WB2Fjfh4Ufgt2r7b59J/GHupD5HyY0eSvBZJK05zG87TDBmn6CUyqR7i40OYKEYlA0HSr2w4kG1J0NpbNgyZ1w8GVbBe4/TbSilv/55Vu56b/fd96pBwcH+fqf/jce/fIKS8TVt84WnmdjZWP5ZB77Fuaa2zAN22HxzaQ3b8RdMAvJi+OvvQ9z5SesxlcEd8ZCmLFw3JepqmhXG/7xvWhnG35/FzI2inoRCEYgUI72tCJFlVAwFR0bI/nyYzDcb68RQDCEU15D4NbP4295Cq9pL6asCn/Xy5g5V8DhQwRnzia1/UXcUARTPxf/0B7ckiL0xF5k1UfIPPwN/LmrCNz8RZwVt71ufD4M9qJ9Z9De0/injll98NgQftsRyKQxRRWYZbcQvOurpH7xDdRxkWgI7egm9ej3Cd/ztfO/PyJIYSkUvrv+5W7tuSVwf6AXr+kg6b2bL7xDJmMT0eYusaT4EnSOJhIjdPl1hC6/DvU90gd2kXzlKTKD/Ug8F6eiGhMtRopqEOMh+K+z6XsdslViHR5E+3bhNWxEU0nbCPo+rsr9JphpiwhcdXHpyfsGE5rd3wqXRHZVlZ/97Gc89NBDtLe38/TTT1NdXc0Pf/hDKisruf3229/ucU7gAw5xw6iXglA+JE7geynMWQ/hcT7GTghCeehYN1IyFf/oWiioQd6o6/1fRU4FDLUgmkG9lHVUMAY/OYQJxTGzrsI/+qolmZ0dSGUORIuh7yTklsFQJww2w6Q5cGArLLgVmtdDZcA2rhVPh/bdaLjQdm3HipCimRArHNdpQjMpGO5DX/13KC+EVAaTl4O2DUHYsdXEqVchx9ZDMAcOrUGnX4+55X/DX/0NONMKpWWWDC6/B7Y9gjZGkWg+ZvlnLenZ/zy0HsQvmQSpNmR0yDbLuGEklIMpqreOAokhGOqHrnZ8L20lDOpbkm8C2X/ds9XP8aCpEQh2wcyroXG9ndi4Ctsfgbx8pLOTYFkuwck1pLs6Gd6yH9cooc2PEbjmM4hxkIo69OQxCOZBTxNm6lL87avReSth42roPMl1S2bzxY9/mP989Onzzv/444/z2IJi7prfi/ggubnoUAITjSCV9RCI42/dinP9RzGvPgyTp5PeuoXADR8Hfwz/xf/ArLwbyb+AjVQWImJDG0rPxb7qQBfa1oj2dWSlHlXocB+c3All9TjZcB7IkuVEAr+tibFv/inOkhtxZ6/E2/MKpmoyfuN23LJy/K52iOaRevY/cactJHVwJ8xYgXQcRXc+j5m5HG09TPJ//jmBe/8Bk029EzGQV4zkFcOUOTiLrke9DNpyAL/lECRHUTGkH/4mwc//De7VHyO99pfIqWbM0pvxdq1n9Dt/YSuWgSCmqh5n+kIbzOC+d1IpTV4hZtEVBBZdcdHtdGSI9IEdpLe/Ak4Ad8YCnPrZl1QNFuMQnLeU4LylAKSPHiCx5lEy/T02MKKsAhOJIRe8LGHUdSDjoCJW/jOWsDZ/H1DNrpvxf/NGE/hA45L+ynzve99j9erVfP3rX+ev/uqvzj5eXV3NT3/60wmyO4F3BBIrt+EKgRh074eyRRffIbcWPbMDwkVI7Sr02Csw46a3ZOX1G1E0DfqPoWO9AKgThXgRnNoHtauQ4mpoCkF+KfR2oKWj4IWRYBjx0mgqhSb6kerr0P4WOLIdSmrQ3k6ksBI6W5DKqZZMZtIw0g7dhyE5YlOeUIiVQNkspKAacYNIfhke2CAJASc/h/RJxUv7OFOqYMPP0as+hxzbYOUDR9ei9VfBintg04PQ3QOjCYi6UDUV2vehs2+AnU9AQSWy4DZbmW3ejhRWwfQaTDQf9ZKQHIRUNn41Lw/cqeCEbJiACbzla6/q4299BIJDUDEb2vaCF4a8UutgoT6k0xDxCZbkE5g2hczhEyR2bWKsbQhxXNy6qbixXEzrYSgrQwproW4R0t+P5peg3W3IrGX8j48s5pkNOzhz5sx5Y/j6dx/l2huupiAIUlCM33UUrVwFZ1oxpeXQ3Uzm6ftwb/sDzM5nkGs+Rvrp+whcezvMr8LbcB9m7g2Y2t8cQft6SF4JknfO/krHhtBTRywBbm9EyqeejWA24tjJ05TpeG3NZI7vJV1UA6ECtP0EpqQcXBe/v4/gnfeS+Om/Erjpc5iiEjL7dxC64178bavR9sM2iGbqQtLf+TrklSBlUzCzluPUzDyvWU0cF6lbgKlbYAMv2o+igz2kH/x/CP7+P+O3HsFr3I7ueB732k/jH9mNJkbx06P4B7aTObDNNtm9mwlqAK6LiebizF6GM38l5iLhEK9BYnGCy6xmWdNpMkf2knz2AhacaiehzqR6AguWI8HweU8Hps0hMG2Olbo0HyK59nHSLU0XrbJJMJT17o0ieeUQDFkbsg8opHTKuz2Edw4Tld3fCpdEdh977DH+5V/+haVLl/I3f/M3Zx+fNWsWzc3Nb9vgJjCB8xAugp79SDgPHeo4a0N2IYgI5NXBQDOSXw+lM6GjASrn/86GJLES25wzehqJlUN6EM2thPaDUGublMyCm/A3/Mw2rwwNIE4Eckqh+4TV3w732f0nLUOTG5GkoGOj6NgAEoqCyceU1497fvU96+/bdQBt2YIGcy2BRVENImYMiYXBddFEBgZ74aavwgvfzxLe9eD50LwBmXkTOmURtOyGUQeaDsDimyAcgqaXUQTCPuy6H+LFUFSOahp6D6LtQ5AYgbRHlmlnvXYVUDDYMZzVIb6uVeBibQMiUFEFp05CfiEU18Lpw7ZZ0fftccaSEMugroMYgzupCjevH7lsJVI1h3RTI4n9PXjNm4h9+X/H7diHWfJh/Ef+EZbfBM/eh3a2UhiEb33jX/jk57543hBO9/TzD9/9Gd/8888iERvGIP1nMDfci7/uAeTDf4Lz0k/JPPYdnLnzkZ1PE/zI10g9/wDujFpk1V3o7hfwDm9A8kts6EgkxzZ+vdaI5EZspT0Yg1AO4gTffCkicaR+MdQvtlXVrb9Cpi9HimvsxCcxAj3tmJCLO20OXmsz7k2/R3rtr/GONWIKizAFBejhXYQmFTH6q/8gOGU2sn8LqXVP4C64Esk9DG1H0cNbMbd+GXEMNO3Ff+UXeKPD9pobyY5lAWbhdZjCcltBr55JIJpH+pkfk37qRwRuvRftOYWfSuJveozAx/8EUzsb4wbxhwbwmvfjHd13XsPaOw7fQ0eH8Xq78NY9jqx/AsJRO2mMXiCuN15E4Ko7z4Y+SCBAYM4SAnOWXPA0qorf1kxyza8gk8aZPh93xoLzKsEigls/G7f+4nIrVbUyhv4u/N4utL8bf2TwLbv6vZ9g3A9SqMTb2KD2HunBejtwSWS3u7ub8vLyNz2eTCbx/Ynlgwm8MxAR21Dlxqzn7sAJJP/iXp0SKcIfbgUvhRRORo+tsyQy8rvpABcxqBuB4TNo0Rxk5BQSzkW9FKo+IgYJ5yAVs6xmt+sUFJWiiTHEDSGRONrbCgPNmMk34nc3okPDUDoXTu9GKyZDx0G086iVLeQUQU4J5BQhgbAlGSWzoGSWDd7oa7LncQIwnICwQUTwjWvTyIYGkc7d6E1/BGt+YAlv06uQ6IeDz8CKL0N3C6SHYWQA9m/C3PR1mD2Cf+AZOHHI2pb5LnL6FKA2lS0StwlyYWPPo2o9QANBK1VQtcEl6dcS1N6QnHahm3RqDBk8BUXFaF8n5BWAzIa2fXYn14V0ym7rhiGVhJADIw7avA1Ts4Dg9LkEJk/GW/MDhn/5H8TvuQujPhRVISmDlpTD6ZPo9PncXSY8ePvtrF69+rxhPPj8Fv7tr74GbgoENDGMOC7mik/gr38Qc+OXYO3P8Vq7cecvxV/zPQIr7ybTsBtHmzAz56NOHjo2DKNDMNAHHSfxvUyW7Kv1SM4rhHgczTadiRioWvwm8iuOC5ffje58yuq+J82GaC5EczGBENK8y1Z5X36Y0A2/h9fWiLfmPqR6BpnjRwjOX4HZvwedVIu76na8PevwDm5Bwjk4c67Eb9qJvvILNBCCQMiuRAQjlgiGY2gsD+3tIPPgP0MmCQgSz8csvQVnwdVkNj+N17SPwJ1/SOqhb6LdHaR/+W+2Eh8IQSQGeSW4dXOQGb+7yedbhuNipi5A3AB+6zHSuzfgnzqOn0jYAJPx0NmB19QAoTAmvxh31W24dXMuehoRwamux6muR30P70gDyafuB8fBnbcMp2baJa96iIh1sIjn4VS/2f9+AhOYwJtxSWR3/vz5vPTSS3zhC1847/Ff/OIXLF68+O0Y1wQmMD7i1Uj/MTSUC/1NaN5vbhCRghnQdxiK50HtSjixBX+8prYLQX0bU1y7cvznc8phpANR30Yax2sgtwxt2YpMudyOYfbV6PHtkE6jYyNAwIZhnNoH4qAjPVYGUH8bcuhBdOAMlM6AnhNQPhnChWg6AWMd0HcUEsNZk39jXRrKZiMl9UjpPEs0m3bBUB9EcsFXTE4M+pJoIomMdCOnd6E3/AG89EP0yqykYbQb2fVL5MY/xn/yXyAWh84T+Ot/iln2Mcxl96CVB9DD66H7FFq/3L7+/g4YGoSeDlAPDQTsuPys04CfJb7GsdplY6zE4jXfXeNcuLkmkwAviegkJDeApvsgkIJJc+HEbohFYWDIEi4/Bsk05ISsN/NwFzrYieSWIqE4UjuTyFCC0R2HyXECyIqPoc//EFlyK/ryL6G1Gc3L5/vf/z5r1qwhmTwXwdrVP8zxzgFqS+JINGyDHfo7kfxSzKq78Tc+irnus7D252T27cG9+Wuw7qcECsvJDCfx0w6BGTW2olv4xsKBQiaBjvWjPWeg7RiaGLVNfm4IThxEqmbBlOWY8yqBBllyB/7+tejYEGaafT+kvA68DE7rQaidTvqVX+EsvxmJ5uJ3dyLBCFo6jWBoO4mOU0hRFGfpjfhN+9ChTjzHhcIpSE4+kk5ZghqMoOEYkk7ZyOzBHmRkECmpwMxYCtOXoCcb8Z7+d5yPfB0zZRaZNfcR/NI/4l75ETJbn8WUVqPpNP5gLwz2w5lWMu1NvGuJjWAnGurbZMF4Ic60hQTv+Rqm+MKWXv7QAOl1j+OdaMTr7cZ/8j9IiVh5wgUsw5z6ObhX3oEJ2gmqO3Mh7syFaCpJZv92krs2ItEc3EVX4JRM2IlN4CIQ3kYZw9tz2PcCLons/uVf/iVf+tKXaGhoIJ1O84Mf/ICmpiaOHz/O/fff/3aPcQITOAsxAXz1IacKxnrRRA8SKb74Pm4ENUE0OYCE8qD+yrf8ndaeZkteJy9/85NFM2CwBU1kdbuBOBRVQ/MOeI3sGgdZcCu69SFobUZmLkaHumyDXW4pDJ9GO7ZjJl2BX7YUOvdCTxe4AXR4DCkohbyc8wMlVNFkH4x2Q28j2roNxYFQHiIOqpms+0Eat7IQb3QIb3AM6RtBFFvhvf4r8NKP0VWfRg4+bQMCWnfCik/A5l9AeQ2cOoT/9L/C9Csw825Aiurwj7wE7XtsGlgsDykth2AMiRXZGGUk67aQdV3IJKx7hJdtUvOz4ROv/328657y4XQH2rEfM/cWaNmNTr7cxhmDTVFT30YhRzKQSSNuAI0B3X34RzfhLP4IAGbqlbgth8l095FsbiFUNttWUCPlSG4+2tsDI4NU+j0sXryYTZs2nTeWrbv2U3v9ZUgsgqbC+Dufxqy4C4nlYZbdjr/1CcxVv4dsepTMml8QuOFzaNN2nDNH8HIqSa591joWFNhGL8kvRnLyzk7WBOANbj9+fwvathftOoke2ojmlNuUulD0vCVHbTuK17gZmTQbmXs1UjUd9TI4Z5qRKdPwdr4Esy5H9r4MkSiZjc8SiOQSmlpN8vBxAsFBJBbHCYTwTx5GFt2Eto3ih2IwNgS9vejYyeygMogxSH4xxPLwW47Avg3gBjBX3oX362/j/P43cIb6SD3wz4S/8k+QSuCfbkGSoxgUDYeQnCrUuNZJ4N2CCBqIocP90HcGb+vzeJuftisjgTdLSQBbDa6bS/hTfwqqpDc/j9d6FN8N2u/DG6GKNu4ls2+TdcyYvojAlbdjwlEkGCKQbYjzR4bI7N5IeuNz9nMRGt9eSmJxpKDYWunlFlxayMwEJvABxyWR3Tlz5vDcc8/xwAMPcP3119PZ2cnSpUv51re+Na68YQITeDshOZPQZL+t3vUcQgunW91sehSSA5Aasl378WpMQXaZr2CqjRwuXfxbNahJUZ3VSbbuQKrP1+ZJTplNwRrttMQ7M2prVfFS/NP7MeVzATA18/F2r7YRr4NdkEjBtCuQkxvRdBId6cRPjWBKZuH3HEIrCqCzH4a60APPW+/VSK6tQr32GvJrkIplUK4w1oWOnrGShK5GWzAzBjwfkxPGi0Xxk0BbIzpYjNQocmY3et29sPYn6Iq7kYYn4UyjDZ2onAXdx2HqIkj7sP8F/Mb1sOxjOHNuR2t7LMF1bcONehm0rxVt2g6+j2T9gImVI+E4EoqO7yBxEc2uAP6+X8Px/fgNLyBL70L2r0FXfAGe+EerNwZIpSGTQt0AogIh1+pgu5rRTNJKRsL5SH4poRxl5FgHbtGrmEW3ojtWw/QVyO4XUSeAv/ERVqxYMQ7ZbeCTV89BwyG0fwhz5X/DX/cA5upPIbnFmHnX4u97CbnmM7gbfkl67cO4y2/FlNbCwXWY+smI56H9J/DaG9Dh4WwFF1Afp6wKZ+7lSGmdregCJn8ymlsF3QfQKTMglUKMQYtmnudiIYB2HEFbD6LrHsAs+TCmZja+n8H0noLKGjJtLUgwiN/XjYZzYMWdyN4XiH32n0jv20aqoR03lMSUTkJ3rUGmLyIw9wrrwiBi3ycvY4lr6xG8g1vw25thbBjcIJgUbFyNs+RGvPv+L9y7/gRd9yjpZ/+TwK3n66AB26zW3wXJ8WN53xGkk/iHd+KP9KF5BUhRKRrNRZMpa9E3Hob68Rt3kjyw1XrellYTvOVTmFie9Rd+I1Txjh/Ca95vI4mP7MXbv9kS32mXEbjqDkw4ionFCV5xCwD+yNA5ec75B0OHh/D7uki3NqGDfdnCeFaw+z6uzF0Mbv0c3FlvrQH0vyrEyNs2QXxXJ55vM34j2U2lUnz3u9/lk5/8JF/96lffiTFNYAIXhYTy0KGTSKwMHTiJ9p+wTwTjkFMJ4fzszX8bGsxBYuWIOLYaPNwG8eqLHf7C5y2djp4+hLbvQarO+aWKGNQJw3AnWjADGetEoiXopAJofBGyZBdAlt+DvvJj6GhFpi1AR7oQ9SCUa29uJ9fB1A9BzTXIyZfQonIkWQiTZloZQ18HDHcDigTC0NuC33kQCebApOWYkgW2gaVpF6TSaDKDqFqC5AtChnThMtyhI9C4H6bPQXQfetVnYMMD6MKbkMMvw77HYdVX4PH/G4Z6EAGtnQ+jY7D5l3g7fm39b3vboOu4bY5S38oRsvpSVbWTDt9H0SwjyxJb1XNLyK89JnJO2vAamffS1nasvAY6WtCdq5GFtyBHt6BuEDJpqxlOWd0owYgNwAgIGsmB0X70+E5kWlaCsuRjyIvfJzZ3KiPrt5Lzqfk2jnjSfPTodmSgBwWW1r9ZC7614agdphuCdDeAlTCs/yXmms8gxZMwdZfhb1+NXPVJ3M2P4e16AZ22BGfFx/D3vABuABEHN15gAztyCpCcAjS/HL/jOOkd62HwMQgHcaqn4EyeA2UzkdIFkBxAuw+goTxo3wpVS5DIuUAVqb0MjRfhH96M37AOqZ6JmTIf30sj0gMDnciUuUh7E37fAJmTLbiJEeg/TXDBcgLzl5Hev5PUjnUYz8Fp3kv6xEE0nTz7HkkoguQVYeoXErj9920VfbAb/8gOtP8MmQM78PZvwhRV4G1ejVM/n8zeDXhN+3Dqz9fmSjiKUz75LX8Pf9dwpi44+7vf14V3cDP+8QM2+nocaDgEgUK7igLoQDfph75pP7sXSj4LhHAWXk1wzuV4B7bhNR/AHx3CO7oP78Bm+/z0y2zjW5b4XhD5xTiTLt6rMIEJTOB8/EayGwwGuf/++7n77rvfifFMYAKXBAnlo24UIoNI4Rty0V9bEs+fivY1gxO2Vb1YBf6ZnRCrPD9a962ct3wW2rEfPdWAVM4790SsFBI2gUvTI0juFOhugFAufm8LptDe1E35NLxILowOo72nYKwZpq+A3iYY7EJDUfyeo5iiafjhMiTTD7k1cPo4kk5mm4Fca0U2PGCXmGMFaHIMdj+C57pISb3VxAbDMDACeaHsNQtjamJoTxPpTBjHGcXZvxOdOc+S2WnLoXk3FNXAwBlk98PwoT9D13wfdUzWOiyJlkyyIRB711jrq1AcwnkwNgB49oYvkpUoZEmtGOA1je5r+t3sj+Pax720JfyZ1DkC7KWg4UW4/iuQHIaBVrRpG6a4xjZPpZK20p0YyZITgbERCBdCjsKIj3YcQqdejohgQjG8cC5CivD1H2X0yZ8RXbgc9q2Fqmk2pCGviGUn3+wys+dIC4lkklAoilTWkHn4f+Befhtm6W34G36JufrTSOU0TE4B/oaHkMW34h7dhndiL5mhfgJXf+p1H1EfxgatJ/JwH7QeQlJjuIUFSFk5GivE6+vD27QWHXkYiRZBrNBeF38/EjRIVw/OlPmYilnnPp/FNRgngB5chw714u94Bll8KxzaiInnQFE1cnwvJidGZt9mnJlT8V6+H+eaTyOFlQTnLSEwdzGZg7tJbnwORgetzMRxMLEcJFiA+EH8hk2w6Umb5Dbncsz8a6xmNZZP+uXHoTKCth/Dr1uImVRP+tmfkCkow1TWY6YuwFTUIe/B1C9TUIJZdQesuuOi2/nDA3jb1+Ad3YWOjNmGu6IKZDzPYAVNJez2m1ZDNBdn4VUE56zCO7jNVnyHB7PEd4slvpNnIRcgvJJbhFNVB2XV75kE0wm8kzBvY4DI+/fzdEkyhptvvpkXXniBe++99+0ezwQmcGnIqYTeRiRWAUOtb35effDTkFuBdh+AskVIIIIUTEc7d9pK7CXDR3ImndUGS8VctH0PevoQUp4lGkUzoaUNkla3K9kKp067xiaVFX7u7NGkdjF6ZCMM9ENZFRosQJJDaN4kGOkFrwE/fwpStRRtWQfaCfn5oC74IfA9RH0gjmo5dHbAyChUL0Zyy6B1O0oKAmEbWpEfAQW3ohC/d4DAwsvx+06TOd6O1z9IYO9OmDUHqZqDpkbR2Aykv8MuT5/ej/noX+Ef3gjN21EnAAEDgTT4QXAAHUUkCCVVaKwICedlK1yKemkkOZxNTUtY8ppda1WylV0vA14SUAg4NmntNaSBIeClf4c7/gK2/gxOHcQvqLQpcGNDljz7CskERJLZynDQOie4ERjphq5mKLX2bbLww+ieJ3GDMTKF1aSHRwh0tSBX3oN2nEB6T1FdVUZ5YS6newfPDiWVzrDn0HGWL1mC0S6YcwOZhnWYvINI7UL8Tb/CrPq4lTRc+1l06+NQOgUnmod/qpnkkz9GIjmWFIYiVpMZiiDhKGbedZhIzF6XVALpPomkx1ApAS0GTUFAkWmrkHg5fncbftOrpLe9CMlnIF4GCBLLJbDyFmTudbDvBahbgv/K/cjKuzBNu/BaGjEF5ZBM4Q2N4OVNwW3fide0G2l4GSmpQaYtJzBnEYE553ysNZnA6+3EbzuG39pE5nQnfjIJ/Smcrmcwrz6Nyc3DLLkBd8YcMof24Hz48/gvPYh7z19AIIipvwy/qx1vyzNkhvrO2pjhvos+uyKY4kqkbh5O2WTILbokDazJycNcezeBa+9G00kye9bh7XgJMuMlAar9Lhljmz69NN7GJ/FefRxi+bbiO/cKvEPb8Y414I8M4rU1jy9JUMDPkPasPh03AO7Fg1k+CHBmLSZ4zV3v9jAm8B7GJZHdoqIivve977Fx40ZmzZpFOHw+Ufja1752gT0nMIG3ByKOrV6GC62/7XgYarPa3uKZ6OmdULkcCcaR8mVv6VyqCj37UfWQaNZbs2oh2roT7WlGiuqQ3ArUVxjrtlXn9CjkTkGGTqLGxR8DeYzUAAAgAElEQVTuwuTYgACZfT16Yrclu9EoHHgBKqsRL2P9daP5SMs6pO4GiJSigYBdOndDNhnOCdvfTRAZ60IjeWgmCX2n0BPbkJwiUEH8tE1achxQHxM0pIeTmL5WTOEUghWTyBzvIH1gE7JlO+7iFLLkQ/Dyz9ArP4/segTa9qGFU3BmXoHWLcbft8ZWUd0ccEcRJ4SGcsANor6H9HejXse5CFuwcgM3aMcejGJLXdn3MWDN8QlGx+0w1uQojLwAY2Pw/HeRm76KbvoJ7H/Rev1KG2iWYHgpSwAiuZAcQ1xBc+PQewb/xG6cLNk1RZPwCCDJASLX38nQv/8TZto0zPH9SGEZDHRiSLP8sgU88dKG88az9UATy6+6DrqacOrmIYEQXuM2zJkmJJPA374aZ9kdNnBh5cfxj2yDwW5M/QKkpQFyohDKsRMRN4Q6ATSdIr11jbW6MgZTWYtTOxtTOf3cdRjuRdsOozuewh8bRPKrMPESmygbioOmkckr0ESK1OqfErz9C8hlH0J3PYUsuhl99WEbbNJ9GC6/E579EYHSIlKvriHwqd9Hdz9v35K8MnTrEyBgpi2DkhprdRUK41bUQEUNLL3u7PdCu9rI7H6FzPFjpNvPwMFvE/mDv8Z0deA/fz/mpk+T+dW3cD/zN9BxDJMcQUsqoKQCCcegsAqCF2gEeyegivb3oIe2kdq+xn5ucvIht9A2AY4DCYYwc1ZiCmzMsQRCBJbeRGDpTRc9ldfRgrf1WbyOE/Yz4LgwNoD36uN46x6FQAgzeSbu4jvQwZ7xZRSq6NgIOjKEJkYgMYomx2xcd/CtTODfZxjHl/p9CyP25+069vsUl0R29+7dy+zZs0mlUuzdu/e8536naVQTmMBbQbzaVnXzxw9ckPgkNDUIIx1QdhnasR2qVrzlKoiIoEVzofeQ9c6NWWsgqV6Mf3StJbtirCXTSCeaV4+MdSO5NfiZBEy7AY68CIs+afdzA0heudXg+grGs/u0bYFJ8+BUA5o/CX+oA6mYD8degHglELA3yED0nO9qrNwmy3lpiLWgxZPQsVFoPGoJlQgqgmQ8EENgZi2ZjgymdwdO3WLcaZMws/+MzJPfJ71pOwEnhFz7FVj7Q3TJ7UjjK9D4Ir4I1K7AWXIn2tuGHtoApfOs1drY4FmbLCmrR4onW3lCctgS48QwmrT/kk6eSwASsUvko4PWe3a8a58cQSvnQdtuGBtFN/8SmX8bunc19J2yVVw/692KWn1wTgEy1m910BEAFwba0dEBJGr9laVqDjp6Cg6vJ+dzf8rY8w/hbd0A5bVwpg/jn2JR3PDEG8az7dBJO+ZMEk0MY6qnQyCIt28DzmVXwcZH8F6+D3PNZ61sYvoytKQGf+czyJLbbCjFSL/t/h/pQwZPo4lRHFeR/BiU1eHjktn5CjqarVqrIuEoEi9A6q9EcnKhqxEprIboIug8gI4OogefBzcPp7SY1JP/SfCOLyJL7kR3PIEsvR12r8GEHfzjhyAQQn0fpzCf5JaNhD/6JyA+uu4BtO801MzGO9OCHNnGazMTieUjJTVQXI2EY2cjjoM3f5YgoF6a5CP/H6Pf/XsiX/xzvKd+CDtewEyZSebx7+Be/2mkoh4Tidtl/1QC7Wq94Hv/TkC9DDLUBXmFuIUlVkcdCKGdrVmLwHH2GeojvfqHSDCMmXYZzrwrLoloOhWTcT7yh8A54uuf9iCnCM3Jh7Fh/PZmqxd2AhckHoJAJIaJ5UI8H6mchOQWZldTPpj3Y1Mx5d0ewgTe47gksvvzn//87R7HBCbwliGBGL6XRHsOjvOsIrFKpHAWenoLpAaQ4lm2wlu+5C1P0kQEimajvYdtBTN+ziPqbJJbtMjG5YpBUwPWDjFaCmKX8/3kMCaUY3eadzNse8hKEConwbHtUFGD9J5AI4XWUeLUVph+JzLtZkiNQHLILsn3NuH7r4URgITikFsJefWYfEFTg/iHXkYFq6kdHrVJZpEoJhwkOK8eLxUhs+95CFfizvII3Pv3ZH7296TXrccliFl4Gxx4BS0sRYZ7oGYxnDqAf2wd5NegSz+K6W1D4sVILN8meA13wWAHemr32SqtuCFLzqMlEKw5V4E568DwmzxWBY5sQycthNbd0HEcLWmCiplwdIdtSEsMQyAAQ0OQW2Kr/uqApwgeGolZ67PmbcjcG+1Rpy5FNz+MOMNIKEJ0+WK0rACKJkFPNf6xPSwfenM3/raDx2G0z/rOnthhq2uAyY3grXsUc8u9yK5n8R/9Z+SOP8WEokhBOebqz6ANa/HHhl93tOz7FwzZ8xZW2ipwxxHUJJG8CJTXQcU0BEGH+tChfvRMOzqQxD/0DAQi1u6NtI2OjYYw4UJIJUg+8RNCd3wRWXYXuu3X9nrkFqJdbThX3Y330oO4OXlkMkmSzz2ExHIIXvlpTDwPf9fzaMNLqAiEokjpZDRehCZHkYaX8ceGkEAImbIAyuss8XUChO76Y2S4l8RjPyUwayne/g04M5ZgEiN4L/wsaz+X9V9+Tcv9bi/Bx/KQ4iqkeBLSewZGB+wE8UKa4lAIZ8GVSCQX/8gOUntegVDMPpZbNO4upmwy8roY4vOI7+kWvK3P4fedgYJyNF5gQ08uIGPQ5CgMD+AP9EJvV1bj7vNBdmOQ2rk4k2f95g3fD5iIC/6t8BvJbmtrK5s2bSKdTrNkyRJmzpz5m3aZwATeMZii8aM1VRXt3ocEcpCypWj7erTySlvtbd+MvqVlL0Xy65BIEVI4A+0/hg62ILmTkXg5DJ2B3HKr221bD4k+u9yoCtFytHsf1F0Fh5+H+R+z4y6sxDMBS/4CYRg4A/HlMNyNxIvQM0fQYBzaNmOqV9ql6tdILefuaapqCfZgB9p12BoQGZezjQbhmA17KM6zf8iSachzcIKjODd+GW/bQyReXEdwXieBj3+NzJM/JLNhLWaJj5tbCeJCWR10HIDEkA2wSI4i2+7LRv/qObpqglZ/mxiyBCYQRoNhMIHsH9GsW4Mb4GyohCr4GducNu7N3Uej+dAzCEX1IE22meyaL4DZYau3iWFsMEPGVo5H+mxlOZWAcBjyXTg1gPacsJU8x7XV9UAELa1BD67F1MyG+dXoyw/AnGWYrlaWz/QxRvD9c4S8ub2TrtPtlEQcmDwPCeUhxkXTCaS4wUYGT5sFM1egD/8D/o1fxpTXI4EgsuiW8T9dqTHobkNbGmyzGgLioLnFSHIM3fdS1tJKrOND/WwkXmj3bdlhPz8FtXgdx/APrcMbO4q78Grc9CGST/6E0O1fRJbcgW56FCkowwz0Ib7BiOInE4RWLcEbzeCdOkHiyfuQvCJCV30IZ/Gt9ns02I227IMjW9ChXjuxCYShZi56usm6WBiDVE1HqucQuOXz8MT38TzBD+aj6x4j/NV/wxS9OSxBM2lIj2PX9U7By+A37cU/ugs9ssWOxw1BvAjCOePuoo4LLYeti0o0jjN3JVJQjn9kF35qHBs19ckMdAOSlSncaGOjs3DKJ+Pc+Qd2OK8R3zPj9CGcPZ5CMIxEc+33Kav9Pms/9gGEU3vxiOUJTOCiZPfVV1/lj//4j3EcB9d1GRkZ4e/+7u/4xCc+8U6NbwIT+K0gIlA4E+07hBTNg+IFcGYrVKzC5Ly1hCJVRU/vsMvJ0WIkfyo60IwONEPhFJuClluO5NegLR6MdSG5NTZVLafSalVzy9GmQfxMCuNmo2CnLEbPHISTx6CmFm3cArXTkd4TUDYTeppRP4OfGMCEx483FhHrhBDOO0eAvRQcXQ/qWSKWSdvkMhRGk5AYgEge9B3GWfFpwrEnSLWexut+jsDyq5Hdm8jsXk+6bgGBmKClK5CKWbaqnByD00ez1dSY1cn6aXufdRyrvc2rsDfkxJAlnpnUWQuy1+t1X/ciLnzxM2nEgFbMgGQAQgWAgXU/g9y4nSyIsZUtY8BR63IweQF0tYAGwCQt4U6Pocd3IFNX2NNOXwFth9DUABouhL4miOWD70JxNfHRfuZUFdPQ2nXekLbuPcztt9wKu59AI1E0FIOCSiiM4157G5lt63EqDTpjCaz9Kd70FThLPnzBlyjBCFROQyqnnfvM+R70nEJPH7OkHWz6GuAf2wGjA5wlNoOngZeR3AqcomrUSZPe9AyBK+7APbyT5OqfELr9XojmoOkkRny8lkM4M5biH9qOth7BqazD/eiX8Br3kDm4g8SjP0IKy3BKKpB4PhKvRJbOxsnJg3AE7e9E976A9rRBKIrGi6FlP/6hjTg3fAkzbxXsWY9zx5dI/uSfSf7wLwms/BCmohYpLLdEMhK31dN32ZXBWXANzoJrACtr8NuOoIc2oyP9424viVFrxxa0wR5+Kom0N0E0hkQK37yDKlJQgqmaht99mtSTP4BMGlM2Geeya5GK2rMrTa8nvheDehl0sBft70I7W/E722wU9QcU4o/XGPg+hTHnBcr8zo/9PsVFye63v/1tPvrRj/K3f/u3OI7Dj370I775zW9OkN0J/JeAOCGIVcHgCUxeLf5oPtp3BCmc8daOIwLlS6wEAh+JliJ5dehgCyS60fRoNsExq9sd7UZLL4Oe/Zbs5k6GwRNQvQSOvgizPmSPW78UPb4t22iXAyOnkd4BcMNIasRWn70knHgJP6ccXNvUJK/X5oXyIVJ8fsCAE4S8Eug9BV7CLvG7jiWgJgBuIQyegngp9B5AFnyMkPk1qZZe0qdHCEyeTCDokmk5SCqnhIC7FyIxpGYBVNRZvaUbsXrjcC6IIokBNGv59pqtm/qXFsn8mnPFuA1qvrU4kzPH0EgeTFkMja/Y6zU6Ctp37lq4IasDDeUhJmhfbyaBOAYtKITBPvTMUbRuqU20K6xED6xDpl8Oh9dBSSWsvAde+g+YNh/aD7N8yjhk9+AJ7vg/Pwq9bWj7QRgZgmQrhKPgBHCuvhN/51qIRpDaqXB0I17LHrjt65hxQjXe8IrP/VpQhBRXYbLvrY70o+1HrMb59cgth9Feu6JQUg+nm3AXLyW9/nHclbfjHt1J8okfE5y7ENob0eIq6OqByz+JHNyMd6oZd96V6Nr7cWavJHDPH+G1NZPetQH/TCt68iiaSqCppL2+mTQSjGBKqyBUBf1dtvLr++hQP4HMj3E/9PtoexPseIro1/+B0e/8NamnHkEAEw3i5uUiOTmYnDwrtXm3IGK10Dn5SOU0pHIqzuTZMPnilUL1PfzmveietdauLhBCAy7i+eNtjRoHr2GjbfyrqsNMmo4/2Etm3aPo2DBSXImZucw2PV4C+RfHtamKBaVQO+e3fPETmMAHBxclu8eOHeNf//VfcbJ57F/84hf59re/TU9PD0VF42uTJjCB9xIkUoQm+9BkP1IwA+3cg/YdsWTtrSBajpQvtoRXFYmVIbmT8bv2WI2u71mSFy6A1AiSHoFgrj1vKB/fS0LZPDi5/ey24rhI/iS0oAyaGpBps63Xbn4xkhhEiqagpw+iFYuQcJFt+Brts5VbyGoej0MojIZzbRRyrAJxI5BbBl2t1pfXDaC+b7lkOARDfVA4DQYOQbwcuvfBvI8Q8H9NqvkwmdrFuIUJ3KBDpvUM6cZGgitvRJu2WaumqcvQwilIX5PlmdFiKJyKJIahp9VW+3w/68EbQ0IxeO3HOJB8XdNacsT+62c9ct8ERa/4CrLpJ8joADq0ES67A7Y/AokEBEP2PIlhCLrQ32+r7Z1HIBq3lmeBEEQFetKQGUFP7Ebqsil4pVNAI+hoL1JwOSYziG+yTh+ldSyrKeTHbxjRtsOtaGoEU1SNFFVbKUn3CfRUI3hjSGI/7rS5eEkHv/0Y5rqvoNsegfv/Gs8N2FfpuFnbqIB1qojGoaACKZkE+aXZCYOimVErD4nX2Aax6ctg+pvdRDSTQtsa4Mwx637Rfgp31RV425/Hmb0K5+RB0idP4gYdJFqI6WrD378FUzIJjZfgvfQg5vLbkf5OvGO7MMtuI3zH5y/4dfC7T5Nu2IoO9SGT6gnc+mkkJ07mpftI7d2F5DxM4KbPk/rVt6Cjifj/8f+ivafwervJHDtApvU4fvcpNN1yzhf7XYGC5yGug4lGMfkFmOJKTFEJEo2Nu4eJF1piOnURTF2UXfk5gb937bkq/Hmn8GGgyzomlNVat719G+yqRVE5Ts21+KND+PvW4298EgpKkap6zIUmAdG4jZvOLbpgpPAE3sewlZW379jvU1yU7CYSCeLxc8bWgUCAYDDI6OjoBNmdwH8d5NVb/W7RbMipQo2LmLewdKo+2rUHKV2ElC+GM7tRNGt5ZiBeYSul+dVQNANObUYTPUh8Mtp7AAnlW8uysU60bBbatA6ZZu2bZOaV6M5fQ8a3aW9yChkeQUMOMtoP+TXQc8BKFQLh7E/2L5IKBPLAicNwL9rfDeYIGoohwYhtLlLPRvb290E8B8IKrgvdLVAyF/oPWr/a7v1IzQqCZjvpk4fIFNfg5hjcWgevL0Hq1edwL78Op7Acv/FVOLQBrZkLoychMWjJpnqWzLpZacGAB8agxjnXiGRcS1ADYUtCgyGIZZvKxrv0Xgb2PQ8rPge7fm3J655fwcyVsOMZ+8c5GLfn99KQTAJJSCpUzob2w+CCaAaNxMEHbduH1i5CxCD1S9Btj1sS0nsaGENW3YNufASmzmP55OI3jWl740n81gbM1MvteygCJbVISa0N+zi5Bz19GOMamLEQb/uLOCs/hxgPvIytgCdHrQvB6CA6OmAnCM0NaON2BEVjBZBfhsxcCaWVMNicjaCusZOZN0DcIDJlMZpTgGnZiz8wCM0ncJYuwz+0D4lXoMf2w5x5MDaEOA7+QA/mli/jP/iPmFmr8Pe8jOYW4Vz7e/jbn0Zyi6F0srWHe+29CoStZ25xOaFr7wTAH+gl07AFv7cTyS0nMH8h6e0bkVghZsmNeK8+gZmxBDNjOQYIXH5hSce7AfU9MscayGx5Fr/lKOmu3ZDx7Pdn3O0VCd2HUz6J4JyFuPOvRMqn4FZ86aLn8UeH8LeshtaDqBPA1C3Az/h4Da/a96S4AjN3Jf7YCJxqwhsvRlvVBq+kRtHEGDiubXwLhu3P+5mtXASmdg7uzKXv9jAm8B7GRcmuqvLjH/+YaPTcDDOdTnPfffeRl3dOQzjhszuB9zLO6nd7G6FwNjLaAf54ufMXQaQY7dqNlC6Cssss4VVFwgWouNDZbMluQS20boDRHiSvDkwAzSQgWmYJ9+Tl6Paf2rAFJ4DklVrf3MkLoHEXzF6CdnXCoKKaRgqrIJODpjPW7SA5YuUDWacFCQatVjYch0gUQqXgOzB8GFQsQYrmQmcLRMN2WX/BzdCyHe04CKUzYPQEOEkIOYgTJlCtpHuHyfgObjgft0bw/ASZzS/g19XhLrkeRoeh9SCE4khOMVTOsbrl3OKsBlPsjTk1aqu4iSGrM02Pcq5rXMBTK7W4wE1axoZRo+j+55GZ1yHNm9FADJq22Q28jK2SGpOtDhvIJCCQDz0nAc82wLkhKI3CyeOQX4K2NiA1C2zcreOikxbD7seQusugoMxqMn2PmeX5xKNhhkbPVewGR8Y4tHkt87Jk97zxGoNMWYTWLECbt0Hzq5iaSry9r9il8vwSq9ENRaG4CAnacAkJnItY1v5OaNmDtjXiP/8jW72bfxNSMwsZPoXvJ5GcaiT45oQtKa4DMZgTu/H7sYR31iz81lYkoHiDSRwnBZNmIKdOQs8Z3Os/ZYnuWBINx9CH/w33zj9C1EcHutChHkglrVVeOml/shIVyStBqmcSWHUrIkLmeCPpVw4QWLSc1PqnCdz8KWTKDNIP/BPOitsw0xdhLuBY8G5BjENg+kIC089FgPv93ehA97jb+6dP4G17Hq/7JGNPHYHnHsMUlxGYNgsTHqfSqoqpm42ZtgT3Opui548M4L/6a6TjmPXzXXiNDe7YvxGGeiGv+Oxn4vxjgUSiUD0VySvJBnLY0Ap9Fy3c3m2YaO67PYR3DhNuDL8VLkp2ly5dSkNDw3mPXXbZZTQ2Np79/4TP7gT+K8DqdytguNVG+b5VpIZQP4V27UNKFljC27kXjRQhqT7US1o9onHwTQCS/Wh6BP5/9t47Sq7rvPL9fedWruqcE9BodEAjZ4AEQYIUKVKkGCSTEh0o2xp57DUj+9njN5pnjydZs8bP8rLH79kz47FFWWNpLIukRJEUxUyCJHLOsdGNbnRC566uHO73/jiFJDYoKpDQI3uvdVkgUHXr3NB99v3O/vYuboboeRtp7PitBre6UN1tvxMA07gUd/wM5A0am0ZCXtRXB4PHUH8IqWjDiF7te2BlCdEJaz6fSNvKrWMgUOjSdhT8AchnEFUUsVVXXMjGkeb1UFSNdr0NVa1A1FpC1bdA32G8JWkyyRqy0+fx1tbhbPgkcm4fuePHyYyP41uyCFm43C7RphMQ64PxU7i5tO2uz+Wv/FI2Xusb6vEU3CcK4RKBiE218/js2Gb7XVJWBYOC+nzQuwepbYHxIUtGo5OQSoPnkoOB2H3PTEN9DRqfRKpaIDoCOEg+gQaKAYN270abllvLrI6boecw6vHihmoxIyeR9fejR17F8ftZu6CGN473XjOsXYeOsOyxd7nfjIO03oQuWAcnX8ORMdRbjMYuotkMZDP2GDIZSyR9YcTjw1Q3YeYvRlbeDSvvtg1Th1+Ak1vR/hNouBRQ1Dlo09fKW5BAkXXdCETsA1BFsyW8PftwpwXtPo+Z34jb24070o+Z32BJeWoSt+cEvns/h6lvJb/tu7hT47jGIfvEn+OsvhNn0XoIF8+qI1VViI6hF06hx7YBiqlvxf/IF0k9/sd42heTfeXbeO/8DORyuMPduCd3W9IMiDhIdeM1zgQfOBwPpn0tpvzaYBpTWgml76zqAzjzF+HdYJ018uOD5F/93+TPHCWz8zUbLDML9OVnkVAIUzsP/8134azagufuXwfAnRrB3fYdGOu3pO3Wh62Xbu6d1negaHwaHRvAHeiyQSSXdNTvJwn6eUc6gZn34/VizOGjhXclu3P+unP4MEGClWhqEo0P//iaXV8REmlCo93o+HFM5VK0egUM70M9Xut6UKjW4i+2WtTkGKZ4Pq6bsZWw4vkQ7UXmrUf3fcPaVXkD0LwS+g7Dqntgz3dh7e3IxXPQfit6ZquVGVR1IoFy61tbgNa4yHQfGh+yZDKfh8lRdHLExvmGi2F61JJPrxc1BnFd9NT30Pb7MRXNECpHDz9tLc28CQiUIcseRo99F19kmEyyhNzkNF7PIKakDu+DN5F74R/IbN+Fd7UiuNYDOJexY3A8SHE1FAUhn7Fa0kwSkkk0VagK5rN2IndzVtt7Pc2mMbaBqHUJEgWtXwLxYSgpVHGGzkI2W2jCC9gqr98DU9NoPUguayf/bNqSbMeB2nroOQnlNejAcaRxKVJagx4dgwUrkfP70dIypOU2dOfT4A+xYV75O8ju3lO9/MZ7ueccD7L0btzsFuTMm0guYyvRAQ9QWDFTF2KjaC6BDo6S694Dxm/vuYIDgxY3oQOnbDJe02IrFUklYHQ7EigFJ2A9hdNxTOMizKKbQAxybg8aVbS3D1NdgY5M407N4BQpEirGzWfRmSmkogHnvt+E7U8jsSncSS+5vS+R3/OSlfEA4jiFB5UABEKYcClUNmLKqpDWNUikjPzZfZhAmMBv/jHpr/9npLSU7BvfwXv7wzg+Bx0bsF67cDnsRGPT7/nH8GcNzedwX3jcSgLA6nWXbkIWLMO8h850p6Ie57P/2u5rNtmB/Qfc03vIvf4E+ZFekk/8d3jybzEVdTiL1+JbdwfOfb+FiOCO9OHueBo3OmZ14+/cmX0Rg/hDSHk1Utlo5SaRko8u2Q3cwCbHDxryProx3GjP6/cR7ylUYg5z+NCgtNUmqmV/DJseddHoeaRiKVLSik6cwp08jSnrsBVTFbSkAZnqh4oFUNEGI0chMYqG65BI0+WKsptPW/eB6g703FvIoo9bQlRUjc5chIalaPdRpKnNWppVLrBd7cEqiA6il6JxVRFvEEJVSO1aq5ed6EJL8pjqebi9O6ysYSJvyWggBNkcqA+cGJz6Hm7Hg5hgKaz/ZfTAkxCpgelecBzMgo/hdu/EVxMjfWocZ/48jJOBiSN4Nqwhf66f7JF91t/TKcgWclmrJ8wdtXOyuaTTdcDjtdVBx7kqUAKsH/Bsv2C14KaQRl9/xZJV902IlNju+dIQjguiBUlHoMQep7qQyUL0Ihosg/HzNpZWDIgXycfQQASMDz3zFjQuBUA6boLRXjQ6hDQugplhZOlt6MHn2TDvnXZSu46fxz25E9P5TinDbDBePyx59zhZAI1PoP3HYbIfzcTA70GWfgLxha0DwInXYfAsEmlEWjZa6jPehSZjqCmFvJA/vhuNT+Gs+QSmzeCe3YFOuUhmCtfNoLEkJhxAWpZjzh4iu/37eD/+S4jHh+e2z+Ke2YucP4pW1llSnc/ZLZu1Dy65rCVw0Qn0Yi/5bKbwHvtw4elci1NSReCBXye7/3VyM6fI7niOXKAEvD4kVIzT2II0toDmcFKJ93QO3xfkc+hIH5pN46oLGNyDb+BuffL6hCKfQyKleDbeh2m+4oRw3VVOEZzOjTidG1F1cbsOkHvjCdyRQfK7nyOx8wUosdZkngUdeLf8Cqb03aUemsta/+ORXnSkD/pO4GZu4Hm8wTALlsO6e2/0MObwc4w5sjuHjxREBCL1P/4HAxXo+FFLeCsWo2OHcae6kVAVmp2xZPLiWUt2y1phYC/k02hyBBNpxJ3pLbg41Fr/3aZ16MF/RNNxxB+2ZOvEVmhoRfeeQJtcxG+QcCWajaMnXi/41Rbsw3wB1PGCURswUFIDxoMESlFPwH6XCirGkpDiKhjrgzIHmu6DC2/A6adx2x/EhMphzaPotr+DRR+D0SNoRbNtaooO419VSWLrrzEAACAASURBVK7rMGblrUj7vcjIOZh4CWPcgr+uAcex+tOiCqhotAWCxJRtwMqm7BguEdMfFZp2NTJJWLIIAgZNZSEfQaOTuDMJskOTkMkgsQyeBeX2IeLSOUqnkcoqdKwLajusZtnn2LHWVEPvOSivxx04gWlYjFQ3o6e2Q2kNms5A4iyy7A507zOsb3on8TjeN0J0sIvi5DTmOmERPwkkXI50bAZA1UV79sOef0Qbl2PaNmNW3Yc2D+CeeAN69iH5PNK2zlaqp7uRojVoLEHulW/AxDDmzl/FtN+Ce/ot9OIMUlMLE1HcuA/HP2VdP9Y/QOb7X8PTuQ6nbQWmfR1SswB33wu2KY0rqwk4nkJkddBakSWtDZqU1WKaFpF5+7vkzx2HfAbnY7+Kp7oe09BGZvsL9iEqFEQCBrf/JHrkLdx47AZXI7UQcOJgwmHE58GQxfgdxHOd6dEEUV+I3NvfRV/5JuIPYpbfhll2C8aZvdHyEkQMTttanLa19vqeO0ju7e/iDvaRP3uRfPdecntfg9oFtjHwPaMEPLN7cX8U4AnW8O5n/kOEOc3uT4Q5sjuHObwHiDcEZYvQ8WNIxRKkahV6cR8aqoVsGpEJ1M1Z3a7jxXUCkHchNoSG65FgFSRHIViNjh3GROrRyla0+22k8x6kqBKNVMJ4P6x7BA48BStugdGTyIrPYEJll8eiqait8sZGbIVnehhGD1oSUlYHFfXgFrrJRVBRCJVC/CSUV8L5vdByB/S9Aae+g9vxKUy4Et34GGz/Gqx4AEaPQXkZxDyQ8+PULyFz5ADexDAiBrP+k1DSbKu62SQkxyExCqkpm+imLhT7oKTWJrpd2n7MZTJ1XTi2DZwg0tQIF88ji2/GmRhGy1y0v5d8NEmu67T9To/BU1eHTEyhkT7EF0HHzyP5NJhi8EWQ9JQNgvAG0VOvQ4P1VJWFa9DJfujdB41tVnri8VFTHKB5XgPn+wYuj8tVZceT32PzynaC6WHMvE7rfxyshEid9Tr+ae85MUjLOrR5NXrkB7hb/xssux9TMQ+z/hH0yA+gcZVtaDuzCyrnocaP+CN4H/l9ci//A+63/gvOI1/CtN5EvvcMxpMjn03gTihOOAJVTUhyCt/9/4z8iT2kn30cz9o7cOoX4HzsWlGyqtoKbiaFDnfDhVNgBBrakdJq3P7TOIEgOYH85CS89W3MbY8iW79J4OHfQqPj6OQ47vhFNJFEvUGk9AY7CKjaJDTH3pduKk0+nUZzeeR6ld1MClMUxymJYMSH6wjuvhfJ73y2oIufBSKYBUtxbvkUpiBNETFI6xp8rWsKxPcQuR3fwx3oxT11/l0qxfbhEm/ANjeWlCM18zGVdXxk3RiKZ0+7+1DCiN3er31/SDFHducwh/cI8QSgvBMdKxDe2nXo4A4wAdTNIo4fzaWtrrakERJj4JSjyTEkXIeOHcWEqsFbhGaiSNNa9PCTaHIKCZYii2/D3fkETJ+HcBU6cgEJReDAt3FLGsAbAm/INnR5/Ei4FiltsHrVmYvo1AV04gKc2A4Rj9XEer3Wz9PNo8ZYQlzdAcdfg5abYGwvnPwu7qIHMZEa3MUfh5OvQusmSI0jddUwPgUmj2TSZE6M4H/o8zCwG4YPFyZeD/iKIVgMkVrrNWw8NgTjUkd5PmcJp3up6UYK5CljvUkLS+PvOOduHm2oh6EhGLwIrevh5Gtox0YY9yJ+P54SgUgRrjq4/QNku/ttxTeZxrtsE8QHobrVyhvSaTvmqkq4cA6qGnEHT2LqO5GGRUjXPpQ8FDXC2BmbWhYdZUNnyzVkF+BI81ru7Gwj9uILeGu7CHzhjzDpKfTiISu7qFz8syG9xkFW3o+bjMKBJ3GNH1l2L7L6IfT0m+ALYTb/EjpyHj26Da2fj9Tm8X7i8+RP7CL3tT/Eefj3keZO9PgOpKIRTeRwEzHMvE50+5O4vgCeJRtwFq0lt/918oe34dl4D6bsSvOYiFz2BpaWFdCywlqtDZxGT+wAN4fUL8QJl5Df/zpuuBg59Cpm9d3o6V2Y5R+Dtgie6xHCGwTNZdHYNBqdwp2ZRGem0OgUmruOY0suhztygfz5QaSoGF9TLZQopOKzP8ypooko2nOI7LHtVu+8aAPOTfdjClpTS3xX42tdbYnvUPd1PXt1ehQdvQDjg7ixaXT6Iu7F87j5LB9VsiuNbfgf+3c3ehhz+DnGHNmdwxx+DIjjh4ol6MQJpGwRUJAVqKJljchkL1S1Q/06OPkUUAMzfRCsQrwhq8Msno9OnMRULkPLmtGenchia91k1n8a99X/AUtuhx3/iK670/qqNi62jWbZmLUyy8YgmYXhI2ACSPl8pHENZv4GVBV3219aIhkIwoz15cTnsx68w/thyZ1w6k0obQQZgpNP4y64HVPdgTvaDQNHoGIehOchRQ2QieEtLyG94y2yz3wFT2sbUtZgx5SKoemLMDlQ0AUXtLqidnNsQ4WKY+diN39VfDC26eq6vT0uEIKyUohOweB5WH4fHH0RyqtgygvplE2mipRh6pLg86PRJPl4ksy2V2xoQNVFnLpanMZ5ID5Ep1G/H3wR9MQrUN9pr++8pejoGXTwNIQDUNcBF3tY31jKt39obHv27MX3f/0B3k0PkHrir5j5w88R/KXfwb/qFjQT+5mTXhMsRm/+PNp/CD3wFLRuxizagg6exD38A2TZ3citv4weegk9fxzm53EWb0TqW8g98WeYJavBH8ZUlsCZs+RnwBRXYD77b3G3/iO5bU8iizfhWfsJyGXI7X2VXHwWOyvHwVQ1YhZ0YorKkKZOaOq0993bTyLGwXPLA+R2PH85qU7mLUXP7IZUDNe9uiFRubEEzd54UlKNqWjAtC211nA/6lNunvz5M+QO7yR19gyC4m1uQYKBWd6sUFaHk7fNmhqfRk+8TXbfS+APYZZuwrPpocsBESIGqW/9mR7lHD5EuORZ/n7t+0OKObI7hzn8mBDHBxVL0fFj4ARtqEF6Crw+dLwHqWq3UoZQBSQnwR+yldziZnTiNKZyqU1dy2eQxjXo8WfQ2CgSqUI8XsymX8Z963FY/DE4thNW3g69By1BvATjsVZm/iIoKkbTo3D8POr4kJp2K6HIZyFYBNNTkJqBUASSCWi/C06/BG23w/AZiANFITj/Bu7MBVh4E5x4FaaGIT0DDaswhYjlwMK7SD71VZxsEZw7VbC7iiC+iI23jVRApLygm41f3i6lpeG61ibLH0YCEdRXMMR3Zrcek0wcPfMKBEohnoDUMAwYaN0MvTsKARbY5XWlkNCWQRa04+k7CwsWoIE63LMHyJ04Tn5gDO/KTsRjoLIKBk5CdTPu0ElMXSc0r4DeIxAfh7K1MH8pHH6Z9TXvJDG7drxNfmgP4gng/+TD+LbcS/Lrf0Hq9Wcp+hf/AVO//irS64WKxYVmvlnvqvdk4ygiSNMqtLQBPfwcbnwK03YTFFWh+55Gln0cs+oe3NO70FP70PY8pnQhnl/+d+S+9Z8xGz4G+1+EUDVkQWemMCJ47v9t3HwWd9t3yX/jj5C6hXg2PoTM4omruaytbB7ZQW5myo6roganuRO56SH09W+g8WmcpRtxTx8AN4vnlk9jVv3oBr0bAXVdm1w4PgAXTuJeqqhe73qoIpWNOE2deD71eQDyA+fJvPksOjE12xfgRqfAzWOKS/DU1WGWzkdScdxEFD21g8y+F+1qTetqPLc9jCmZ3fZsDnOYw0+GObI7hzn8BBDjgYpl6MgByEbtEm56vFCJLKBhI5x9ASLVMNWF1KyxGtp89or/blk7WtyA9u1FFttuYimqQDrvgAuH0ZyLTl5EGhZdOwA3Z6OA3Sxk4xCfBKMQiaBTvZBMQjCAOD7UKQQtlNTC0Dnk5mY08igc+ieoWw4l1dB3DEoiMNkNmRhUNUA8CekYOnLayjNqlyCRKoIPfo7kE39H8LP/fHbj+0sIX9EZC6DZjPWYLeC99KmJzw+dD6JHn4fKahi+APkoTHfZim4oBLFp61ebilqy6+bB5K0VWzSGiQimuQ1nTTX5A/vJvLEV7+ZbMQEP6vVCsBg9+QbUdVoyWdeGDh5EnQiSnUaNw6qaEF6vh2w2d3lsQxfHGEhX0FhWBulpxJcj9LlfI3fqENH/+nsEPv4w/tV3XCa9jJ24js2a2jCJWf5ePEGoWmKb764+L0XVsOZh9PAzuIlJZPk9yKr70UPPwZK7MB0bbSjFkW24S3NIWTtSVouO9lvNddsi9Mh+ckPTyIGXMJsewThezG2fRW99BPfwVvI/+B/2XIoBr99WHkurkYompHkp3k02CU1V0ckR8j0ncLd9H+/Nd6On96CZFFJVT/5iP7LzGaSozHoDF1dCcaUNRQgXv+PYPmiIMTatrrQGFq7+ke9XdWGsHz29CzcxDQhS0UDgvl+01myzIRgBhdzpQ2R3voJ7+hSIwamuxtO2FscR3JkJGDxJ9n/+fuFh9jrTswg4XiRcBMUVmJJKpKzK+mt/VFFRj6lpudGj+GBgeB81u+/Pbn8eMEd25zCHnxBiHNQThPSk1e3mswWpQgLxhTD+YlxvEOIT4PPgZpNIUcGGrKQFN5cEdZHG1eipF9CpfqS0EQDTsha37xB03ArHXkYHu61eMhC0dl+BiNXIVncg4Rprp5YctYQvPWkDHNS1aWMKKoqEStBkAvfCTkztctjwBXT/P0CoEppXwfmjUFYOiXErT3CBqmUwfh5qOyE6iDt0BKlsxX/PwyS+8VeY8ipL4FXtZzIz1kEBtcTzUmOa4y0khwWvrFrr5f9cH4pNj0vFkdQ4Tk0YMzWONCy0le7Sapgch1TSkn9fyBK0iTErc4hOoeUTSKAKpobwLGrHLN5E9uVv4SztxKmuspXcSBluNo3x+pG2DWjfYTi/D+qawecnkM+xckkHew8dv2Z4u/ceYN4jj0Dgij2Zt+FmimpfI/nK98jse5PwA5/CBIuhpOm6y4SibkGzXHhVF1AbUtC/HaqWIoGyaz8TLIE1j6CHn0X3Poms/hSy6gH0wDOw9tNI4yJMqBj34PPo0jRmy6Pkn/wzZPO9cPBVjBMgn1Oo78Dd/iQSKkGW3ooEIjgr74CVNtJac1lIRHGjEzDejzvcDUe34mbT1ge5sgnpvAnP6i1oy1JyO57HaZyHpBI4JVWQO0p+chwTLEWCfnt8sUn0Yg/Eb5zHrj24gsWdz4/UtyJ1rYhvFinCVRAxUDUPqZpX2IULYwNo79F3DYJAwKlrw/PY7yFeH24yTnbP62SO7kbTKcTnx6lrw+koQ+IT9mFtNuQyaDppnU6Gz5G/cLLgW30dv+qPAKShDd+v/PsbPYw5/BxjjuzOYQ4/BcQTRFNjtkErM42WNSET56HWdvhTtwIu7IGiRTB1GqlaiTvdgwASqYf4IBJpRCNV6MBBKKqxwRQAm38VfvAXsOkxJBND41FbwU0lIDoKmfNwdBvq80JZDTQsRqpX2GrvxLDlkdkkeD1WSyvGJpIdfB134TRm3nJk3efRQ/8EF09AdT2MDEFVja2eumkY3A1NayE1g04PQv1yyCWQqS6Cd96K5FKXq9kSLIPiWghVIsbYCnY2ee2Wz8DVccFw1f/PglzGRgw3b8LtOkB2/xtk+wbg5AW8C2sx+bz179WE3YcvbN+fSsK8hXB6BOJR8JeDetCiUszERbyfeozsi99Bhy/i1JQhgQiceg2W3WvTxWo70P4DaHWjjWTVGBvWLH8H2d312g94+NMPXblmWKmBs/ROQgvWkn3iz4j+zX8j9MAjeFsWXmdpvKBbFXnnayYORfW2Wm8uWNJ7FWEWbxBWfRo99jy659vI6oeQzi3osZeR5Z9AyusxGz+Du+NbyMotSHk1OjGKpBPIyi2Y43vIbn0a36f/BbhZ3AMvgquYJZuRstrCPe6F4gqc4grrUnEV3HwO7T6Me/RNePvb4A1g1j9Evu8sTnYaNR6cjtW4IwNoPo8On7fXAyAYLqT93chpSJGyaqR2AZqKoftfhIwl8cySGgdAMIKZtxjK6+1KgBjralHV9O7f5LowdBbd+xxuPouU1uBbuxn/bfcDkB++QGbbC6QPHS5YDP6oMpsHKLL3/fXUMR8ReKsX3+ghfIB4P5PyPrwNjnNkdw5z+GkQrIT4gNXOpsbBMejMRaRAdqVono12TYyDce0k5wmi2QQEKtGxwzYgoGEV2rcX7dmOtG4BChZm6x9CD78CpQ3215DxQ8APAQC1kbPZtJ2gj2xFsy9BuMQmdInYSmswAplxSMZsHHA8BSNduBf7kbYVsPJROP40RIegug4u9kPzYshM24pxzw6bChephMkeNBVDmlZbFwhfBNEcOjWIjnZB/zHIxGzYhhjboOUpxAV7g1dCCtzcD53IqxqVHK8lmN6A/UzpfOh5G9O6GX8oiE6uQo+9jHo9pHcexgQ8eIOKuHkuTwTGwMyUrSRPTUFJDPGXockEODlMuAr/rZvJnjxO9mQ3npYcJjUDy+wQpPMWtGcPjPRBpBSiY6zvXPiOy7/rra3okRfQQjVQAkVQtwgJlWHCpfg+959wtn6T5BsvkXoFpLgUCUWQcBgTLkIiJXYrKrMJZcbYZXVTCONIZ9H8tH1Q8JfDwLNIRTviu9ZTVWvWoOe2I6//PWbjI5iyRrT3IDJ/FRIuwdz8KO7B72E2PkD+xceRzlXQux/Jp/Gs2EzmuceRQBBP8wKQHO6b30BTcev1Gi4t3FNhJFyKhEqsl3JRJcbxQNsauwHugZdw9z6LtG7A1WJk/DzuUA+mdj7EpsBfihYXQyZjyW9i5oY2xShALAon9kKy0Izn8yPV85HwdSQJeeDCGTj2NgBSUoXMWwJlNe+quxZjoKEDabD6d50cQk/twI1PI4EIpmUlgV/4jfek3Z7DHObw42GO7M5hDj8NfMW2SS05BmKQ1BhXqy9FBK1ogdEuqOqAqTNQ0gIzfUhZB/hK0PQU4i8FXxA1fnTkNFJtJ0RT04EuiqKZ+KxfbxwvGqmF0X4IlKEeHzh5OLcHysvB54NICUyNQ3IaKWmBinnohbMQBO3tgt5jsPFXkJPPwMwQNC+H8wds4pcnaJvHpvtg5gLkrfuEnvi+dV3IZcAR8Dg2gCDstQEX4oDmrooEzoE7DWmbUnW5sns5ZKJwzlTBVfs+dW0lWL3IorvR7jeRltusTGLxHciFnQQW1ZPHS/rAccQbx7ewoC1189a9oaYW+vsscSsqRZJxqGuFnl2wcCNeIF9VQW7XHqSsClZdwKloQjxepH4xOnIcqufBYBcb6t8ZSbq3a4A//9t/4ov/9o8JNraiySj0H8bNJJDmdUi4HOeOXyVcVoubnAGMlZIk4mhsFHesFzeVsofvC4AvjFTUWYKphfORT6Ppach326bEvrdsNbGo4VqiqMVodBj363+C1nTCeB+UvIn4wojfj88bxbOmBgmFUG8ZMnkAabsFd3QY/2N/iI4Nkt3zGlJaiffe30F8fjQVh7EL6GgfGptEY71oJgWyF/wBJBBGquZDQyfiD1mbsckhuHAcN1yFVDRiUjF0egJn431IsMgONZOC6Bg6PVaQvdwg5HPo5DCUlFhLtboWtLga7T+DzkzO+hEdOEdu5AI4XkzHGpzahWj/SfTom7NXYy/JUkLFmOZlUNloK8JldciaOvuWZAztOYSe2GbDYepbbZT4HN4biiqQ0uobPYoPBpceht+vfX9IMUd25zCHnwIiclm3K07I+u36i9BUFAnYhhEp70DHzlk9rSagfDGaS1m+VzQPnThuyW7jWuh+C01NQXHdlc/PW3fdxSXNppDxbtSbh8oKxFdsdaqXIlzVRXxF1mM3GwcngJQ1g/GgAz0QSUDtZnj5r9AVdyFT52DwICy+C068COt+CYkNoRhbMU1HrX+wp1C5FR+Xq7LGWwiOEKzgl4JmFzvZX9alznYizexLc+rCzAh69gVo/QR6biuy8DY4vQ2NxaG4CKe0CqejEXdyiuzARbSo1nbW5xWK4zA5ASMJJNyDb83NmNgUBH2QjINxcKqrkY0ryfeNkvm7f4+z4X68m+9Hlt+FPrUNFm4AhBY3SmV5CWMTV3Sm2WyWL/3Nt/jrp1/hy3/4r/mVL/6fmLbNVj98fi9uOo40r8OsvgeJTVqHjB+G69qo4vELaCqBJsZt012oGDEOOIJ6g5DNg1+QSCUkMuAdA6cCCVbYqmsgAmvWoxM90HsAPvMf4eBzyKpPork80b/8A8LtRzHLbiV/6G2k2npBm3iM7BN/gTQvx3f3o+jUOJlXvo2Ei2xQgdePlDZBdZslwD4/ko7D4Gl0Zhw9uw9O7bTjLa1GNjwIb38bSU2S75vEKS1Baptxj7yFJmOX7xcpq0Gqm5DKxnf5CXufYRxkyS325zibsWEZ5/YjqcT1QyVKS5GlG9DKJtz9r5J9/nHr0FAzHwnOHm5gFi5HqurQ88fRE9svOzrIgmW2Wh6MIItvAUDzORjuRvPX8fmdwzsgszZ+zmEOVzBHducwh58S4i3odsO1ED2Pli20ut365fbfHT9aUmdlAhVt6NQZxH9VRdd40HzaevgW16GeANqzHRbd/SM71cUbgNrFSO1i2yQ2M2wrgVpoEFNFxVtoInORSDWM9UJFsy2qTk5A71a487dg69fRhkakrAnOvgSL74N930Jv+gKmymsn38nzqK+ooKWNgy9irdc8fptElc+gbt6O2+O3lWFPwEoSPIXNeN/zUq2qohNn4dxr0PUsFLegZ1+H1ttg79O2KzmbgXAYE4/iKy+Fxk4YxlaIfX5oqoD4DDq/nfSenQRWdyIdN8Op12HpJ6HnbaS0FE90Bq1fhhsdI/21L2OW34JBEI+Nk5X4FFs2ruSpH7z5jnH2XRzjV/+Pf8Nf/M3j/Ol//X/5+Mc/jrTecoX0pmJI43J7Tn4YjgNNnciClVYfm4rDwCl0tM9e41X3IIGwPR/ZBJoYtude/NYiLZsCpxiNTaD9p2yldP5aZMffw7pH0SMvIKsfoujX/xUzX/2/KfqdLyPH3rLhHwOnMb/4ZcxwN+6JHWT/aSdUL8R36wP23EcnIJO2DVEzk7iZNKSTuFNjiMeLs3QDUteMTF9EL5xEh7qsrCYQxGAgniA3MoInk7RV4EixdWEoKrfNh1OjVsd7o5DPXa7gSjCMNLZj1txjr8N1oOqifSfh0KsYj8G55zGobELPHkRTiXd+wHXJ73kJjY4hpTWYpTdjFi5DomO4J3ZCYtreXw1tSFOnlY40tH+I1ZNz+KkwFxf8E2GO7M5hDj8tglUQ60c9gYJVUx6Nj10zWUl5GxodtiQxOYzWb0amu8BfCsULYLoHyhdBdSeceQUaVkLvbmi+6T0PQ0SguM5ue79nvXZR26zl9UEmba2sgiUwcgZqFiECGi6DXX8PN38Gdn8fLZ1Gyhqh6yVo2QS7HscNVEDHFkxlO0K7bT6LDRdskHzXbMZ4bNNaLnVlS8cgPga5FJrPvCfbMQtFSpvRZb8IR74NiUFLYo88YeUSbhoScSiKwFjBdix6EcqbYLIfEjFoXACTY0h0Cv+qTlIHzxAoq0HKG2DgIARLkGQa9XoQfwDP4rUQ/ATZ158k2zeMb8lk4fyl+KPf/Txv7DjI+FR01tEePnmGe+65h3vvuYevfu1r1NXVQest9roPn7Tje8chFmJ4c2n7sHCp+l1SBNk0uvcZtLwRWXyLja0ubobERTQbg2AQypthsgtpWIRpW4uO9eMeeBldsBzZ+21YdCd6ZhtOx2ZCa9YQ++ZfE26pxY0mMOEw7pvfxLntMTzzl6GJKO6hV8l97y9Rbwgq5tkGrnAxUlaNiRRbjbHXj6ZT5I7vxj3wFhIpwbNiE6auDe05hGz6DO6rjyNGcUiRn3RxVqxASsvtOZgetRKGVPzGTrCqiBEIhJDKRpuktuMZW129XmXXdZHKBszquyEYQc8dRM/sA8dzXZLsWX8nNC7CPXcY99gO8rueR8pqME3tOGvusfsZOIO75/vWWcHju26DnAQjdtn+koXbu9n/zWEOcwDmyO4c5vDTw1tkdbvxQXD8SGoCRdBcxkb7AhKoQCMVMH3BxvVGu1E3bydbTxA3n7aSAzFQvwKig+B4r7Ej+7EQLrYktzgCqUmr2x0bRkfOYJbeCx4/OngY6pch071o571w8ClY0AHnL6DeBOLzwdAh2PjrMNELJ1/EzSShqNYS3xLbfa7ZFDp8zuqS01FUs1hdrlPQlDr2z47PSh2yKa7t+r20BDkL6cmm0FgUwkXQvAV6tkNZmSXPAYFY2jbrOQFbxc1kIDEF6z4Nb/6d/d5UEvwhiE1hqhvxr1hC6rXXCDz6m8i5HdC6DrqHoKwCJsbRnj04W34L32d/j9SffcE2WAXDkE6ybEknB5//3/yHP/+f/K/v/eCH0sCu4AcvvsiyzkX87Ve/yqcffsTeB40r3tOlu/osaDqGdm2HXAzd+g1YcitSvQDCdXCpypsaheImNDYM0X6oWYm57VHct59Cy+qRs29AxUJ0cgBPZS2Bukbi+48TKsmiJoi4cdzX/87ef82LMY31SEMdOj4MExfRoWFwXdycC94g6gRAPOAP4XSsxrPqVjQ2Tf7IDtzpCTzz58PgWcwn/iXuG/8A9e3I4bfJv/VPtjENsfpkjxcTDCGBH51Y9r7CF4DUDHKxG01EEdV3d2Pw+NBMlPzpHbbijSBFpVBcMbtdmIKe3AY7ny7IZhqhrg53uI/8gZfJ73rOylCCIUxNIxIpttKW/CxxwYBOTsPQWdx0wjqzzPYA9RGDtKzEWX//jR7GB4O5yu5PhDmyO4c5/JSwut2Q1e36ytBcAmlYCQOHYP76K+8rakBjk1ZLmhyEsg7rjRuqRiKNtgGseD5SVIM7chKa1sO5tyFc+eM3q1QvhJ6DdonfeGzDE8Mw1lMYSw00rUN7d0PjCmSqBxbdiw4cgpoQPy0FggAAIABJREFU9PaiK25HBvfDwX+Eig5kzaMFre9ROPgdXDeHNQ/1QKQC6lrsfv3FgFoHgVyq8Jq0KWr5NJjSgoxBLp3A6x6G5rO2+S5YDZkJG1QRTUHjEohNwMgwhEOQF0uCLw4DLnJ2l9W5ShomR6G6Ggb60FgM4/fgW76a9PPfxn/TWuRij0108yatVCOXRVMzSKAIp20luZNH8LW2w+QImkzQML+er/7+r/Gv/tOf8Ad/8Ad8//vfn3Xs49NRfuGRz/BrD97N//P1b1FcWjbr+94N4o8gS+5GJy6g7n607wjatQ9Z/Qmr0b1U5c3MgLhouBoGdyP1GzAfewx33wu4KTATPWhiGmlZh3fwBKxaQfLFJwh0LkIiDZDLQd1C3NgEcvEssnA1smItkktbyUQmjqZmYHIQnRpBp8Yg7uIeGiF3MIj4gzhtK5D6BeQvdOFMDCIVDZibfwF359N4P/dl+3OSTqCjF9CRC7jTY5BOoNnZvGk/IKhCKg3pFK4qSMAGQ3j9178vsxlkOoYpqcLMq0FaV1upx8CZ2X121UUDijTVIPOXoNMjMNaPaerEc/c/g3ye/LEd6NQIbiINmaitEF+3WcgDnmK7hX9mZ+L/15DyeTd6CHP4Occc2Z3DHH4GEG8ITY5BUSOMHAR/BE3PQD6HXPIRLZoP0/0wcdbKFVJThbCHaiRYgRu7AG49YrxI03q0fx+ycDN6+mWbwub4CrZchVfHd60v79Xjmb8G7doL6Sz4XcTjv9ykphMXkPImJFQGLbeg596CxlUweRaparOktCUMe59Fb/+CJbzjJ9CRIza0YcFtyMZfu0Z3q+pay7H4CAwesH8ZKINwBQTLEF8D4vgua5BVXUuCMwm7ZVPMFjAhCNpSA127kca1aPN8OPkiDJ+zldzLiWnGpqm5Lrg5dGIA2m+FEy/ZalswAjgwNQRNi3C8glYWkzk1gL8hDJU1MHrWRipnM+jZt5Bl9+Hc8ilLRCobkO4jMDmKVAqu67J06VKee+453nrrLb70pS+xe/fuWe+Nrz/zEm8u7uB/fflLbNpyOxIutaQ9XGIb0N7L/VXeBGUN0LsfnR5GD/wAFQfpuAkprwd/CRoftg9P4Sp0cA9Svx5n3b24PUdw9z0H5QlM8Sfg1ATeRUvJty8jMzyCLx3D2XQPMjEMI+fB50f79sHxV1Cv31bjfSGIVCCRKkxLG1Q0oCLQvQfTfQBNjaIHeslnDJTX4zYshN3PYG7/HKZtHXpsK7LsdsQfQho7oLGD93bkHwzUzUNixoY/xKbQ+JSVnsz23tg07uQo+ZkEJAfg3AlMpMi6fXhmn1JNUwc0tMP5Y+jIBcQXQCoayB/fjiSimKomWHsXjA+RP7XXupi8iyezFJUixZVIaaWVMwRCc5ZlHxWIef/s+m5wmuH7iTmyO4c5/CwQrIKZC5YAIJAcQRpWwOAhG8oAltiEytHElF0GTvZDsMI2dBnHWpFNnoGKJYgvZAMSMnFk8X3WgiufvfY1m4SeHbiaRyrboLTx8oQnZY0F4wPFJnFZSYGiMGRja6ViPuKPQNsd6Nk3bBV67BiiBilqxl3TCG8+jq68Dalogeiw1ZV2vWqbdEKltlKbTYO4V7xhfdZeiukJmDxlySdql3MvE1q5shwncv1fsqrW17RlE9q1A1mwCV3xGTj8JOLx2ECNbB78GUt0vd5CNVsgUAQY+3AwPgrlJRCNofE44uTxzGtDBwfIXBjBV1wLKlBeCgMD6Mh5VBVT2YB4HNxUzpKzC11oZSXgoqqICLfeeis7d+7kqaee4otf/CIjIyPvOIyeoVG2/PN/w91bNlNZEqEk4KU04KEkHKI0EqKhupL2pjrm1VRirqroiePYtLxApOC4UAHharRvP8xbaR0Rjm9FGhYh81egToHwBsvRob1Qtw6zYLlNiHv5r6HvUOFhKUzg458m8bf/hWzrncipsxCM4Gz5POSz6MkdkHfstXFzkJqB+Dg6mMPN5ywR9AeRygXILZ/DKSrHHe2GXd/DjY+Qe+swntVr4OW/QhavR6d7yf3gL645J+IL2IeL60XsfhAQsR7CwfAVsugBKfFwvelRKkLIvFoYHcId7EU9WTQ9TT4xdR1HEYUzBxA3hyktxdQ1oZUlaP9BdGIEPF7USaMv70XcPKamEWrmXdcNQl0Xkgk02oMOHUFjMwU7uI8u2XUWrMRZedeNHsYcfo4xR3bnMIefBbwR6zQwcwE8ITQxhqlegdt/CApkFrDLzuk4XDwKNUsgMWqDG4oabdiE40dTE0igHOpXol2vI2132qQs7yzaxqo2W9kc60LPvGYrcLVLbJSsx1PwuFVIW3JCMmH9TfNHQfNIZYuVSLTfYV0O2j4GQ3vRqUFMZQu6+G701G5Ysg78PvB5gJwl3Ora447UWfLu5iGfLFRpASds09suxwb77DFcjhAuvIp1O5g9vUfh4iHo3wr1S9Dut6D1dmjZAudehtJiGI9CIG29eSurYGIcUtNwZhuUz4eJ89bKq7kNJk/D1CA0LIb0JN7qYjLjYVJvvIynsx0nkLLuCLksOt6NqVyIRELke7pwjIHJYQDMwrXovueRdZ8ErJTlkUceYcuWLfzGb/wGzzzzzDuOxHVdXnj9nU4OVyMQCNDW1kZHRwft7e0sXtTBXZuXURUJQCqGxiZsU5frhZ3fQf1FEK5AT+9Gdz9rbcIWb4AKtY4fQ/ugbi2mqglXHdzhMzgLb0ZHR6A8QvCmm0iPTZGcmMG3pBG2PYsUl+Gsvsu6Aly6Curah4hcxjbNTQ6hg2fRsQvos1/BNQ6U18KKO3FO74YF68i++T38dz4EGR/OLb927VVVtccRHYXM7NrUDwLq5mH6IjoxdOUxzBe06WjXsREjVIIprYFmcLBWYdp9GLf/7HV9di1BjeMmk7jnh+BMN6aqBrPkY0hjO9p9FLLnbSOkrwTtG7y+Z++lcQL4yzBVbVBcYT2MP6qEt+Df/JHAVQqw92XfH1LMkd05zOFnAKvbDVvdbrgBjQ3ayl/9chg8YmUCgPgiqNdriaETsjZhiYtIUaEJraQFHT0I/lJLkCtaYeyMDaS43ncbB6o7kOoOGz4xdAx1fLapK5mEkmJbmSuK2K7/5Ah4POj5/bazvLoV8fhh3jro3QUtm8FzEO0/jCzYCCpo9wnMwpVo5UJkph/NxCA5bSuyHi94ArZKHCiHQCkgtuqbS9lGm1wazSWsLZbmbDU24xbIeL4wic/i0ZDPWJlDxSoYOwTFIeh6FdrvsfKESBGMTBSqugHwAcNDEMja71r5Sdj2uCXWWReKQhCPo8kYIhnwVuFbWIIWK7lMMem9r6Cu4Pgn8IS2YTYvxCkvxx2fRB0PkozZCaG2BWbGcU9ux3Ruujzcqqoqnn76aR5//HF+93d/l3h89jCQ6yGVSnH06FGOHj16zb1188038+CDD/LAAw/QsXRt4V/uR3v3g5tDFjwCgDvWj+54EgZCaHERtKxAhvejtWuQ1XejR15Gl90N5/YhVWvQuoX4ZRBfiZ/MUA/piSiBpQ3o1ieRonKkos6SXp9tABRvAHxBZN4STLO11lN1YWIIt2sv+urfoxvuwRk4jNxyC6nn/wFfZzuqU0jRdTTLN3oWqipBqq4k0mk2DdMD6FR69vcnZ9CZKStPqp0PlXVIRDCLWt/1azSfw4yPoKNDuFElP9RNrusIqIupqMRpWww11ehgLxodQ2dd7bj6Z0TsNUmOwkjhAeLDzFbeBaahA6fj1hs9jDn8HONG/5qZwxw+NBBv2Op2Iw0wMwCJYaSoDnfwsCWVhUqNBCvRygAM7oPaJTB1Ds1nrmhaSxbC9DkobUMqFqC9u3DP2YqgFCp5hCps2tYPVXLEF4b5G3DPvmGTv3qOW1JoUuArAaZh/mLoOQrVbeiZt1E3j6ntQMKVaFENXDyB1K1GvRHc068h7Xcg+TwaS0DqEESqkIaNBa2s5/IYNBO3Uoexblv19RTifr1B8EaQYJWt4maTkE1c86rM7mqA44PaVhg9DuEGS3zDaeh5wQZa+ABykMtDqd/akHm9heX3DNKzD/WGgSQM98K8Fug6BVMDUL8IMlMQzSD1bXinRvBu2oibncE91U3m4AGy4+Arr0Fm+sinDB6TtlXoTBzTvh73wIto/ymkcdGVayDCF77wBbZs2cJjjz3Grl27fqr7SlXZvn0727dv50tf+hIdHR08+OCDPProo6xatQYd6cI98QrS+TFMZSN6++fQfd+HyoW4+17BLSpH8jmkYxPu3ufQc/sQ46ChOiSThCBIzXwC/gv4dJT07ufIpZTAkiU4JguOzxIvNdb2LZuxjWU/ZOQvYmDpXXBoG2z8FE46gf/Tv032je/gzR6Gmvk/h5VHtU4Z5fVIRSOUVmPeo45as2m09yjafc4qaasX2NWTWSDlDZiicvghYxVNzuBeOE3+2C6y+/ZC8g0kUozTuRYJl8y+L38QwqVoIISkk9YnODGNzObx+xGB8s50ww8thPfRjeH92e3PA+bI7hzm8LNCsAqivaibsQQhNoiE65C6pTB87HLIBJFGSB2xVUtvMRgfOt2FlC8GQPyltjKcTSDeEDJ/ow2AUIVMzAYJjJxCM3EURTwBKG2CorqrdH4KDSug6whkcxDwII7PLqfmDHRuglM7oLIFTr6Om0tjGpfb6nD3dnTmIqayHdcXRk9vhWQMUnlYcgcSLkHPvmYDKjJx3EIqmPhCUFQHTessqc0lC4Q2CekZiI0UpA8FAhyuvPzn6xEMzefQc1uhosMmt+VzIGUQSEB8CnG9aChk9aVuDqajUF0LY1Y3qyPd0LoRTr5mq78lTRDqsZrHZAwxap3P3CzER2H5Q5gj30RqqnAqlExsDLeyCePpI68+0BSayiCJUfBHkFV34257AhMuQcrqrhl7a2srb7/9Nn/yJ3/Cn/7pn/7YVd7r4fTp03zlK1/hK1/5CjfffDO//du/zafuvAXvke/DkruRogrY+Gl051M4mx7BHT2Ne3QXZnoG09iGdh9A1n0KGTyHFnth6X0IAvPW4gDBRBT3xHbSxw6RPnHcBmF4fVYHfTkBTm2TZLDMOlmIoK4i6X78joM5vROpWYBTVocu30Q+Po0kbKIfgASLMHXzkdp51yV1HxjyWZgYtPZ5p3ZcsZO7DqEQjxdpW4eU1yOta6F1rf25Gu21+vUfhiratQ83NlkIj2hHGhfx/7H35vFx3fW5//tzzuybNJJG+2JJtiUvsWM7juPETkgoCQlxAgm0pKWFsDS3l8KPUMiFC7xooe0N/fXe0gVoGi4B0gKBAAlJCGDjLI4d24kd77stW5Zk7RpppNGs53v/+IxlG9uJHRLHkHler/HIozPnfM85oznP+Xyfz/OIy4P4w9gzL8OeqdV6x3Fw9r9E/oVVWmE+fWX6N5BNF5wffnM25PeYrbwMrHQa2i575QWL+K2RyWT467/+a55//nni8ThNTU3cfffdXHPNNQBMTk7yla98hSeffJJcLkd7ezv/9V//Beg17B//8R95+OGHAbj99tv59Kc/fUGaK4tkt4giXiu4g5oYluhUwupkMZODSKQG59h2MHMRsbQCJi5MzULo2gBVs2F4DxTILgDRmZjB7RC79ETTmQh4w/oomzZ1WTPZFMQ7MYef05Q0lx9sDxKqVoKcy+lyxoLG6ZjBbmTCD3Ougf0btElo3xocwKqfB81XYvb8ElqvwYrUweI/wclOYg49D89+G6emCWYt0/S0UDmSncRMDuOMD0L/QZhM6MBslzYfFciSeANa7U2NFZrsckpQc3mMyWvj1EnL4/IitgXTrkS6NmHcgQJhNzDeA25LtcilpdA3AhkXYEEkDN1HwR8EJwOBqJJvr6UEv7IJjuzXRLvYDMiNwkiPhlTEewA3hGwYGcEVKyPbN4rHIzg+P2YyjozFITAE0WZERO21nn4Q68p3q27yJLhcLr7whS/wyU9+ko0bNzIyMkI8Hmd0dJR4PE48HmdwcJADBw6wd+9eRkdHOR+sW7eOdevWUVNTw59/6E7+fGCImqW3IsEyWHYHZu1DWPOvxVkQwdnwS2ThrfDUdzEmBz37kJrrYaILfOVT6xSvC3vBNQQWXKONT8PHMIPdMBE/seGKRmicg5Xo07AUQAJl5NI2Eysfx9OzCU+kGtmzDnvxzZhNz2IyGRDBKq9CSiswjoOzbR0mOX5e+/zawpw5wtrtOUWzfDLE8iEd22DHM0goisxcgoSiUNV81q1IwyzdWi6L6dmH2fgYTj6HBCLItHlaWRbBsiystkW42ha9FjtXxO8lXkef3XO4WcrlctTU1PDggw9SW1vLM888wyc+8Qkee+wx6uvr+cIXvkA+n+fJJ5+kpKSE3bt3T733oYceYtWqVTz66KOICHfeeScNDQ3ccccdr9P+nECR7BZRxGsEEdEp88khqL1KG70SnYi/AqmarQlaNXN04cg0ZKIHk50AbwlYLpyJXqxgta7LcmlwwHi32pm93HbdPojNRGIzAY2U5cDTSCqOsW2t7KJNahKqgtoAZmQC6diGmb4YOXYQY+dh5yqccAyrpAamX4M58Ay0X4+IheX2Q9t1mBlvwVn1TTiwBUNOwylsL+IJYpXWQu0iCFXol3Euo8146YJP6+QYZNIaAOENF7S+Xq0QWrZWqzJJSCdhPAmZQZz0OPQdRJqXIKO9mHxWbdQijdDXpwcgEIZsD2R9UFIKY+NKgHMZ1RTveabQqNYBqaySkmOHIJVSP123W7dbdwl0vAj1i6F7PWCwUsNgKvU4h0M4Yw52fw+UV5w4/rYLa9kf4az5AdZb/vSMKVrBYJBrr732Zc+jMYaBgQH27t3Lvn372L59Oz//+c/Zv3//K372jh07xt/87d/z919x857r/5O//eL/pHnxW2H5H2PWPoTMWgLT2zAHNkFFDLNttU7bu8Na1XZyZ/5suVxQ2YBUNpw0Tgf6OjAvPko+m0ZCJdC8APwl2IkBIm9dysRGm/GffAf/wgXYg/+Ga9EfIO4GdfEYHcHp2oEzPKA3Ym9oglrhH7cHq6wCK1qBlEQLoQ5nPiZmfIz8kQOYdArx9GP17EVw1NXBcxaCXBLTJjIR3d2GZp2tmRzH2bUaxoaVw5RWQlWTOlUUce4IVWrq45sBb3CoRCAQ4GMf+9jU/6+99lrq6+vZuXMnmUyG1atX8+yzzxIKaYPn3Llzp5Z95JFH+OAHP0h1tV7n7rzzTn70ox8VyW4RRfyuQTwhreZaNniCGAGTjiOl9Ti9u6B6tl7wPCGc+DjULoajG6GiFfo3Y6a9fcqLVoLVOANbIXBmL92zjsEd0KjXRD+EI5qyZEpgshDwUL0IMVsw2Qgc2IRpmqt63e7tmOe/i3Pdx7A8AahfCB3roGXZiXVbFtZ1H8Cs+T6y9A8R36mu9sZx1I3BVaiMub0QKtP3vorjaYzBeekxzEg34naph2y8UyOPswZsKRBmgRwQ8cLhPmhqhH17IOzRRLX574D1RzRJbuc6qJoG3YdgfABT1oCkEzDep81ylbOQo+swPj8kxnBfciO5JzdgNVRj8o6GIsy+5NQ4aG8Aa/EKnLU/xLr6j1/VtJyIUFlZSWVlJcuXLwfgn/7pn9izZw+PPvooP/vZz1i/fv2JOOEzIJvN8r0nVvPI6nX87Sc+xMe+9E/Yy/8Y8/yPkMZaiB+AbAhnqBMzYwlybD9Sd/bmxzOOE6ClGlo0ytoZ7cfsex4z2gfRcqR5DsF3vJPcJfNJPvgfuGpr8dmrkcaZU9V7q64aa1qTfk7sN95x12TSmOEh8gPdmP079HN8tnPo5LFKSnHNmQfGIX9oPyYxChN9Z/XGtfq6ELetFeOqBojV6XdEKIiEdEbHGIMZ6YeOrTjZzEWob754IY1zsd8sZPciw+DgIIcPH2b69Ols27aNuro6/uVf/oVHH32UyspK/vIv/5IbbrgBgP3799PefqK/ob29/Zxu5l8LFMluEUW8lvDHYLQDJzuOlLVB31bMaAdSuQCpbIP+vVClf+ziqwB3AJMeBW8UvKWYvk1I9eKp1Um0DTO0XXW+Lr+6HljnQHxFMPkcVFTD4X2qlcwnAUt9WGPzkMHtmMZ50LEFU9uK1M3H9G7HrP5XnOs/hRWuxEwM4hx4GqlohUit+gG7PHDlezAbHtHQgd/YLm6ferWepTL2snB7C36yIfAGwR9G5t0Am3+GmbUchvdCeQv07lI/X0kimRQmElHrMSev8gks8Hh0PJbA4c2a7JYehVQGaqdrddfJqx4ZB8b6NdL3wBrwloE1DuOjWJUxMmNJXPXTyO19CWeoH0tQtwlxFUzeBSmJYbVdgfPM97CueOdpNwKvBiLCrFmzmDVrFp/5zGfo7e3lkUce4Rvf+Abbtm076/uSkyk++b++xkOPr+T+bz/I3CXvwuxaCbEyzOhRJFqO6dqFCUbPm+z+JqySSlh8KwDO5l8iGQ9S3YKnrB0bN6k1TzK+tRPr4ACukjCu6hrsaCEIIZ/FnCli94LhxI2DJRZW7QzkkjqVFbyM/68z1Etu50Z1ZQjGcC+4CamomdIkn7oJB+fYEfIHt8PkBHQewzrWB6ESpLIJmTb/xGelDph7+iqKKOIELh7vsWw2y6c+9Sne9a530draysqVK9m3bx/XX389a9asYcuWLdx1111Mnz6d1tZWksnkVMUXIBwOk0wmpzzLX08UyW4RRbyWcAWUlI51IuWzwbIx4sJkxpGyJpw9v1KbMBEI12MGt0HNAujdoiRurBMzdgSJNAFo81m0HbIJSA1BLlWI6QUwiNjqc+spPeXLQnwRTKIfKW/FHNytbgW2pVZgpTORxBFMWTsSP4BpWQgd2zTuN1QFdhyz+v/gXPdXWNWzIdsC8U449JwGWNhuKG1ArrwdMhMwGddHKnFSaMSpENsN7oASw+wkJncGb1WDks9cCjMxBqOF4IyJCWhaCAdegEtugGMvQkUzHNkIktcGs3AExnog7YFYLfQcg9o66DqqgQzH9sKst8De1RApg13rIdYIA0fBdmN8QSQ1oX6dRzbBwncjW76rVdTBQ1iREkxiGPH6MakkxohWmEMnNQWKIJWNWJEYzvqfItPmTdlzvVaorq7mv/23/8Zdd93Fc889x7/+67/yk5/8hPxZCOOG7ftYtOQqPnP3X/LZmxbjbamBuZdjnvspxOM4TUHkNbzQyIK34Tz1IFZZLeLxYV/6B3i79+JdWAVNC8j2dJM+uAunczdMjGq4yVmm/i8YRMCy9BjYFoJBTO4sn2SVdkggjFU3HavtMkQssrs3YUaH1aP5TLAEy+vHar0EK1aH03OIfNcB2LMF2fk8EolildciLQtVylBEERc5HMfhnnvuwe1284UvfAFQn3C3281f/MVf4HK5uPzyy1myZAnPPfccra2tBAKBUxp1x8fHCQQuTPpfkewWUcRrCNXthlS3C1DeDkN7MKMHkdh8pHIm9O0uyBm0UY3SaZhjm6F+KaRHMeM94I0i3oiu0x1Qonh8Gydtz+SzMNGDGTuqutdAtXrdhqtgYhiCVUoiszlwuzESQHo3QtVCJDWMCdcjE/2Y5jnQsR0z+23IoQ2I34dZ+zWcJR/E8gQhNlN1wYDJZTT2+OgmTXnzl0J5K/jCWGfwBjXGaENadlJlAu4A4vKe8xec07cbjryEGRoCVmEvuAkzuAcjRr16XRYESsHpVP/eiD2V8IXjFAhIFoKlqhfOJSGZhtlLoK9DNcO5DDgpiHdBqBySo2q1ZVvQuwf3giVkNm/AVVKBGezEmDJw0pj+zVqVcAdVW+0rQdw29rLbcfZvJv/cj7AuX/GaazBFhOXLl7N8+XK6urq47777uO+++xgYGDht2Wwux5f//6/y8E+m8eBXv8iCtnIoq8OMjyATo+oiUDntNRqXhbXkVpz1P8W+WnV49ts+RP6xf0ZGjuFxufHEvFDfrjeCTh6TfRUzAK8VjANGvapNpAK8YQyCmUicPS54bBhnuA/nwFby+zdjxKXvC5VD/izBKKkkJj+GHO3G9nmwQkHEspCKJigpwxkbwunuhr1bsPw+JFqpko8izglS34bVdMkbPYwLg4ugsGuM4XOf+xyDg4Pcf//9uN0629jW9vKzRDNmzGDPnj3Mm6dFgD179jBjxozfasjniiLZLaKI1xjiCWOSAziOg+UOqNMALkxuEimbhrN3JVRMVzlApAnGDkNsDnS/AJXtal82tBOqF2uj2stty3ZDpElJqJODiV51cfCW6hdXtpAIlslA0I8EGyDZjenfipS3I7ZPPX5zaUwsBlt+gpl3C9K5E/EKZvfDOGXTIdKovr6WBVgQjukjl1LyOHkMk+w+azUMUPmFZUNWHQdebtlT3lc+DUKVcHg9pquD/KpvYl35HpU5OBbYaW0mCvghY8DKQ2kFDIxAdTXE43qzsO0X0LQADq6FkjLYswliDTDcBcEoBIOQGITp18DOX0DVJTC0GuL9yKLbMGtXIwsvh4Ej5Df8Ctcf/dUJS7iJfoh3qO2c2w+RBmT6fKR6mjautV+J1M08xz0+P9TX1/PlL3+ZT3/603z2s5/l61//+hmX233wMDe87+NseOT/MG3+tbD6QZzJNLz4BNKyACmrhWjNy07fnwskWIo0zMbZ8zxW+1Kt8L7rUzA+oul9Y4OQGMLkMkr4vG9kZVdjrPEGELcFuYS6ibycZjfiQarmQFULzsgg5lgHpMcRK3WWtGCDRPwa4RyuJptIk+06DPk8ZngMV2gQywaxvRCpJW/bmHgSMa+NVd2bAXbJBGcJHC/idcAXv/hFDh48yAMPPIDPd+JG/rLLLqOmpob77ruPu+66i61bt7JhwwY+/elPA3DrrbfywAMPTNmUPfDAA7zvfe+7IGMukt0iinit4a+AZD+M7oNoOxKdgRnrhPhBqJiLNC2Bzo3QsgzxhHHi+yF2Kex+GGTqNMdOAAAgAElEQVSeWphZbszAFqTq3L0jxXJp7HC4HmdwuxLLdALCJaoVjJZghruwZr8Fc+gZjHUQCdcikSZM/ADWjJtxSvbCjscx1U3I4DgkDFg92nxjwNj+QrSvAcutpABBO/Fy4KQL2mAD4taIYLFBrDMnQhmj1V7QAInjDg1y/GePSkJwYPpytR4b7MZ58QnwW4iTActBnCwmFIbBEXDCEC6Fzv1qkdXTA2EfTI5ByxI4/KJ6AE8koe1S6OuEiGBcfmQijiQHMPk0NC2F/WsgM4lVNQN3NEBucBQrbzB9ndqN7/VphTpUBaEqJb8jRzD9u5HSWrC9yJW3YvZuxNn3vNqpTSWSaRoZHj8Sa0R8gVOPC5yIVj4HRCIRvva1r3HHHXfw4Q9/mL179562zNBogi9942c88L8aoLYFOvfBjCVIeS0Md2MOb9PZAgppXCUxpKJRNaznUWm0mufjPP8TTLwfKa3UZqxIBUQqLkonWJMcg6EuzFC3Nqe90vLeAMT7kNFexO+GMr0ZPGPzoDGYyQnMxCgMbMHtpHFHo0i0DqltJxtPkN23A2dMLdjsklLsUDmm2KB2zjCvgT7+dwZvsBtDd3c3Dz30EB6Ph2XLTjQv/83f/A233HILX//61/n85z/P/fffT21tLf/wD/9Aa2srAO9973s5evQoK1asAODd7343733ve1+fffkNFMluEUW8xhB3EOPyw3gvprQN8ZVihvfoa/mM6mktF2ZiCAmWa6Naehgz7S3QsRqZcSNmcCuEGzAj+5Hoq53mEcBANKbEzxhIdCO2B1regul4FmN7kNwkUj4HRg9ieW1My7XQu03J5f5nMccbuZpmIvlRyDlKyEVA0jrdm8+qHtf2qJRCLMhOQC4NJlOIBXY4Uc+VE8+Wpc+5pEb6GufE8vks4GBis9UeK1aHePyYrp0wakFIVLpg26rNZRByNrhS4PNDIgllZZA3YHvhhR9B3Vw48iKUVMC+HdrENzGi5NxlMIl+qJsHh9drk1s6A5aFHfaTHejHtiyskJvMyoewKutwXXatkrnjexRtgkgNpusFxBtBQm6YNh1pnY2hYMmWTasNWzYN6RGcl3ZAelLtp8proWaakl+XH/FEVH5xjhe4ZcuWsWXLFv7u7/6Oe++9l1zuVJnAf/34Cb541zuYVtGImRzHbPwZTlUz0rYEmXmiOdIYBxLDmGP7YPdzU1pxsd1Q1440zTujzdrUcbj8FtXvXvunGq17EUMCEQjMRhpmv+KyxjjQe0hvDIyDlFRqfHS8H5kK3Tj5DQ4k0xifC8pmQGkNZmQQZ6ATDu/GdrLYJRVI2yVI01zyQ4NkD+wphEYUcS6w3sgexzcZ6urqzngjfRwzZszgoYceOuPvRIR77rmHe+655/Ua3llxcX8DFVHE7yg0jjWGGetASlqQkhbM5JBWd8tnQeNizL5V0Hb9VKOaFbsUJ1CO6duumtt0HLAwk0OIv/wVt/kbI9AAh+wkUhrDdOzV2GBXFmffWqyZV0HLNUp4S73IyD4onwtOFpFDOIM+pGc7tL8d6d0LZQ2w71mVLlxyM6QGINEFwWokWAX+8leUXLxaOIN74fCzEIlpBdVnIzXTMAe2YaRMtc9WtlAJtjVFrapM/XeHB6C+FjoOqgXa4GG44g/h6FatRCfiMP9KeOlp8CQ1EWx8BGYthhe+p76pZkzty8TCLivFjNrI6DDeD76f/NEDZB79FvasRbjaF544+rYHaboKM9qF6duL1F2mNltna+Br1WqNcYDBTkznPsxkAvIpcLuQmplITRviP7e0MZ/Px5e//GXe8573cN111zE0NDT1u3ze4R+/u5J/++uPIqVlEKzEjPRi1v0YPH6sptlIec2JCmxpDEpjU/93clk4tgezfZXeBPlDSPMCaLoU66RQDbFdWAvfjnnxcWTJO8/5fF/sELGgZjpSMx1Aj93BzZhUQmcxToNKJaRhDpRWw9GdMDmIhIJI61yI1uD0dOAc2g5bViMCnkj5y95IFHEqJP8maup7gyu7v6sokt0iing9EG7UlK+xTihpQYKVmPhBjNsHTk6JYcV06N+LVLWD2KqdbbwKs/cxJNqCmezDlF2CjB3CSXSecTMitgZUFJrZpl73BDGOrVUliQJGm9S8eSRUjrP9l8jctyHNBcJb3gy9G5GSaUhZO1ZpK+b5BzFH1yKt16ibQXkDuPyYtd9GqqYjLVdog9H4EPQdwCRHNM7YyR8fnBJub1BDJHxh8JWAN4L4QudMjq2KNpxIA+x8BLJHoHKuJpz5XTCeBq8paCyBYAjio1BXC96k2o5lc+AP6M/uAGz9JVS3Qfd2dWY4clCrwFYe4w4i8V5kcB/GG0KyeQwGZ/AwuLy4muvJ7N0C+SSmrwO7YTpWfSv53S+SfuSbuBa/FbvuRJKWlNQrQe9+UV+wzyIFcPvBE0I8QYg1ItXTpxr4nIkR6NyM8+KjBY2yB8TSpjdfEHwhta7yBcEf0TSvAubNm8fdd9/N5z//+VM2960fPMbnP3IT1bYXaV+MVM3E9HXg7NuA07kXejqwWhci0Wq1g/P49NntxbZd0Hr51LqceD/sfQ6z8j7ylgV1MxFfEKmYpjdJoXKczp1YjXPO6Xz/rkGi1chlN73sMiafw3Ttwex4Wv/+Kxpg2iXI6IDGN2cmsWK1yKK3YSIVON0HX51135sVZVVv9AiKuMhRJLtFFPE6QNxB1X16ozjj3VihOiRUh3HSWt0ta0MqWnH2/koDJSLTYKxDfXWr5mG61kPjUhjeidRccVado8mnYewIzsg+xBNS4uvygacU8sMFSYCt3rOpNPh9mHAJ4puD2fwz5NKbkearMYefVQu09DDO2AtIWTty5Qcwa/8Ljm5WIhWKwWg/EghiBg9hBg9qBRur0DnuApNTHa2IdqlLWi2mHKMXeVSi4IijUgdLpioVZ/bjd6BxMVK3COa/F3PgaejfreMJRrQJLRAAx0EswXj9EDYwOgElERgdhYFBqIpBX6827h15Cevm/4FzbLcSuKFjUFML/d3AMfC4MalRaP8Dre6KBd07lISODUC4DJL95FY+gASDWC0Lsedcg922kNzGVeS2rsVzza1IUKucYruhcam6WJzJhxWjThWZCRjvh0wHJp/WxibLhdRfhsx6q57r8S4QF8ZfieQdTZtLjWPSEzDSCx1bMbYbWfj2KbL80Y9+lK985SskEompLabTGb763V9y78f/VM9lvEvHWlkJZVFMzyGcrU/qbICTPyErMQUtr2WpdtsXBn9EddbRJiRUgmQnof0yGOrE7Po1JpeFzc+Te/ExsG1N/AtFkUgFhCv0XL5REDkvLTKgf0v+EvCFT9jOvdJmbBfSNBea5hbs7Low25/BSU8ggRKsuUvAH8Z07sQc3KwzAGfSuBdxRkjA/0YP4cKhWNl9VSiS3SKKeJ0goTpMOq6pVaE6iDRAzwaMx6dyAcuNNC6BIxuRlqtwRlOQSyFl0zGjh2HoIHg9p8QIn7YN2wvRgiVYJgHxAzi5lDo0OCl1aMCBSKFJLVYOB1dhai6Fmcswmx5BLn0HMu1qzKGnYNpyJNIIw3vA5JHF74I9a2F4FMKWEt5gOZLoL4QxFDxaRcBCfYa9lfp6dlI1qbksSA6sQue7BeIYcLKYbIFI5fNnSQZz1Bs30YM0L8eaeR3m2A7MWLeSXHsAkwexPOq56/KATMJQPwTrtGI7MV7w8HVU5mDcOIc2qldv/361JEtndZwmiwmUI2MjSK0bY7t13+LHYMbVsPOXuOYvgvW/wFr6HvLbnsV0H8A6sBECEezL34nLV0LmmUewYrW4LrtuinS+LKly+yFQduK8Fp5NahTT8QxUzkbCNVDSismMIeOd4CtHImXaRHbSqkzXHk1yu/LdiGVTWlrKRz/6Ue69995TNvmN7z3B//jQCqKuMFI27dRtlzXpT56AkllfSG9s8jk9p9kUZnIchg7DyFGc9CQyOYg5vBVTUY/seBpr8QqkvlDNXXAzxslDLqOx0SO9MDaA6dmvRP+NguMoGT8Of0ijfUti2jx4JqRyMNSpvtKnfWbPgyx4XYi3BJOexKx7CJNMqPa7vAZKKqaSFIt4ZYinaNNWxMujSHaLKOJ1gviimPEucAVxkgNYgZjaD7n8SoDLZiH+EoxlYZLDSFk7Zng3VuxSqF2MOboOwnMhvg9n/GjB1cAuVHwKBEoAXzn4KxBPGCrmavf3wBbMyTKB0nLoVN2p8YWRYy9B6RBccj1my+PInLepxvTwc0jrtUjFXEw2CcO7oTqGIQajA9C5U/WHVU3q1WocmBhFxkcxmTw4biR3vFvEi6FAQq28NpgJ6i4gNlgu5HgTne7NacfQZJIwcADTtQvSY5jauUisDQlEoWMlxl9oQgt7lUy7rYKcIQATk1DXAGMjMBSHWKW+5gnDzl9j3fhXOKsOqEtAz37V+JIHy6t2ZAP7VIZhW5Adh4b5sPFhXDOqyebzSFkl7lv/gvy2NTi9R7BaZmHW/wSTSuC+7kOY0VEyP70f19IbsGuaXuVnqARaroPe7ZiRI1B/GeKJqJfzeDcmkQB/FeLyT1VlpL4dK1CizWHL/wjx+PnEJz7BV7/6VVKpE2Ee4+MTfO17T/L5z35e3S9OP/qQjGOGOrV6PFWVLpyvYBlSPR3aluGybEwmiendh7Pmhzj9h2DgINbS26BEo3E1QtuPePxQcvFNO6uFXBwz1A1DXVo1P9Ny+SwYg1Q1I9Pmv6YeyiabwXTuwPQe1GbGIs4NpZk30dG6CIx2fwdRJLtFFPE6QvyV6r06sgcCMShthd4XVbubz6gzQsNizP5fY7XfAJ4IZnIQ8VdgglUwsBuZdjWQV2cCp/AoEA5jHMhnYGQ/xuS1iugr16lny4XYLozbA8Ew5B2wg1qB88eQ0aOQHoe512F2/hqZ/VakdgHmyDpougpxB5CqRSd2pgrMDEcv9kd3Yo51IN4QEm2A+iVIqOxlgyK0CmbOu2Ll7HwKc2Qz9PUUKoojqoO1vOAPQeIYBq9+TVsCvgC43TCRg/G4yh1GhsBVCuOjUOID243TfwCiDTDSqd/xJeUFKUMXeD2YzIQGTrjdkMpg+wLkjQGXCyvkJ/ONv0JmX4nn5o/AnCvIbfgl+GuwLluBeeoBpLQa983vJ//Cr8lvfx73Nbci3vOfbhURqJmHScUxh56BqjlIuFrdOrLjMN6F8ZWrbrsQJS1lNVhLb8N59vtYS95JVVUVH/7wh/m3f/u3U9b9z999gk985L2EYg1n3rgX1VzzG1IDsbQaHz8KR7dinBME2Lr2z3B2rMGMDuOsexRrxlwcU7Co+11B0KOPM2BqLxJdmDWbcJwc4g1q85nrLJdU+/yazaTifBtS39yQQOCVFyriTY0i2S2iiNcTgUoY2g62Byc1guWLgjuICVRCfD+Uz1FbpvIWTP9eiM3E9G8CXxlSPR9z5FlM5zok2gyekCaWuf0nCKNxIDM2Zd9lXEElw6lhdXkIlEM+pw1rAMkEXHIrcugXGCuMZMbg8BpovRSzaxWy6DaktBF6t0LNpaftjoiFuLzQvFAf5wElwudPeKw51+Lks5hju5HhONg9GmYRruC4xpZ0GiwfYhmMywWpjN4PTKagZQ5sfgZGxlSbm5xUZ4ZNjyJv+0vMU/+uOtzxhGpUJYeJ1CLxQY0PTqivsJMa1/E7eazGZiRQhbNzA+l/3Ijr5g/hXnYLZjxObt3jSMtV4Dbw07/HvvQGmLuE7DOPnuLhKmIhkShSWoEECk1m/oA+uz2n3TiIrxRar4PerSrjqF2IuEOY0ulKeHPj4K1A3H5tYAtEsK55H85zD2HNvZpPfepT/Pu///spVmTDI6N880cr+cSfn6ex+/HIapmEiEc/m/4KwKMWXKEQZqwPEx8gv2Ut1or/77cOq7ioIFZBO2lhiWDifZgj2yF5htQ142BScX1brBFpmouc5FpRRBHnhWJh91WhSHaLKOJ1hIiApxTji8HwLqi9CspmQv82cHsx+TRie5HYDJy9K5HSBqSkBeIH1V832oLJpVTTmR6DRA9kJ3EwOpVquVTPGWpQz89MApyMToHaHq0oJQbA8qkX7Ug/pAehdQXS8SQmZyHuPBzbggkHYfdqrDl/gEmPYYYPIWUtb/QhBMCadz1OPocZPIwM9YM7BPYYks9jXD6YSIC3IGWwbbByWpX0xTQON1IG8RHw2pDNACmwXJihIxCs1JuDiXGIRDSNLZuHRDdUN6vkwnEw/fs14jadQCobwRvBnncVMtRH/on7yD/9I9x/8j9xX/8+8kd2k9++DmvZ+2DXati9FteV79YKYAHqZDGKGRvFmRiFoV6NlZ1MYrLplz8gqTFIrYSSeqxIGRKrQ6KlSK4b/DFtVrTciNuD9ZY/wTz/ExpbF/G+972Pb3/726es6n/f/zD//VN/jfd8kszyGchNajpgLqUuHIkeyKe1SbGqBFz1MDwIFWU4G76pGliXOjqI26tOIli/gxdYc9KTmcr/wC48zgDxezHGYCY6MC9uxeS1oRJ/GHzBc47OLuIsiNRjNZ57AE8Rbz4UyW4RRbzeCNfD0C7AwskksDxh1c6GGtTftkIz3aVlGebQGmTm2zCJLm1WK50GvS9h0hqxKy4/lNYjhQoxuRQM7MbJjCMljVDapE0vI/tUyiBGu/wtL4RKYGIMBg9BuYHWm5DDqzCJESitQdIJTGIc58AzSOtyOLoR4w0jwdgbd+xOgrXgJpwXH8WMHkMGusAnUFanTg+TYxgnjxgLXBa4XVp5mxgBVw5a5sHm1ZARKC2ByTSEymHL43DVn8Ha7+qNQTgKfd0w1qPT9+4A4jgYS6B3t3r9jg7A3LcjR7djvGBF/FizFpDvPkL2vk9hzV6Ka8V/x6qfQX7jL3Fc5ViLlmE2PX5Gl10xDpKa0O7+qlZk1pVQWv2KBMgkenEG90PZbMxAD86B3TgjA5AcQmpbcM9dioQLjU5X3Ibz9IPcc889fOc73zmlsaqn5xgPfv/HfOQjHzn3k+EOAtETjXT5jIaC5JJTjhNSBaZjOyaTRRrbYOAoTIxh0nFMPosjnGhuvBhgWWC71D3D5Tq7G4Jlv6qQDMFoA2WZBqPg8mCSCRgf1htVXxCiNW+sO8XvKgIXx3fUBYHwOroxvD6rvRhQJLtFFPE6Q8QClw/jL4OhnVBzBZS3I8P7wHZhcpOIy6/TzxXToXcnUtmOGdqJVC5AqhdoG5cxGnM7OQLxw5jshFpBiUCkFpNPwZE1OqVseTTBrKAVFHFhomUQH8Yc3Y+kxqHWgea3IkfXYIa7MGWNSsI7t2LMBFK9AHp3QP2i03x83yjIoltg408wE4MwmVCyZKHpaMkkhCOIbTBSqODms1AxDfoOQrQKhvsh7VbC66QBC4Y79WbAbUN8SNPWMJiSamSgQ9ctSRjphrpZcHSLXm8uXYHJZTHd2zHdO7BTI5hoCfk9G0jv347nrntxLX0HZnRQpQ2x2ViV9UhJOYSjp6SuGePAcBdOx0uY538E6QkIRJHmBUjrojOSKwlXY1k2pn8HVutyZLreNDmOgzn8Ipm1j0M2j1Vegz33CqR5Pm0mxe23387DDz98yrruvfde7rzzTlxn05y+0nmxPTqT4C099Rdz6zBbf4U4buTSGwv2ZflTn88StHGhYTJpmEzAREJJ6JnS0AAmEjiJJOIPIs2zkdJzI1rGGMik1IpvYhRGB5HJhLpUAGasDw7sVlcQt7doPXY+aJwLl9W90aMo4iJGkewWUcSFQKQJRvaDyeNkk1juAI6T0fjQkf1IbB4AUt6Cc+BpJNOAeEsxyX4koOlAIoVQBHcAInUnqmpOHiaHYaIX4/JgsgnIJiBgg+1GxMKEKmA8rM4CdgkmPoKkXoDmPNQvR+z1mJFupHw6pgoYOAa2gGMwx7ZiALHcUFIP4ZrfIGpGk74yCciMYzJjgKV+vy6vRt3aXv3ZKgQivMrKhIjA5bdhnv+hNqxNDCAuP8br0+Qzx9FxW2h1zu2CxJhOL9e0QHxASYQx4LEgGIV966B2NvRsh1QKomVqrzUxrB35voK+Mp2ElsWw+xlIjkD5NMTlRpoWQtNCzPggzo6VuDITOIlxMl/7OHLFe/BcfTPuGz+AM9CFGe7H6T2CGY/rWE+BAZcbKZsBHi9mMg7bnoYXfg7hCqR25omKr+3GKq+GUAVSdYnak027GrFsLMuClsuxalshk8CZhNympyBchj3exWc/85nTyO6hQ4doqI7xjrcs45ZbVvDWW24nWPrbN0mJCPalN+BsW43zwq9OvG7bEC6HSDnieQObiywLSioRb+C8i1pmYhRzYDPOgT2qsW6YhXjPsi/Raqzjrg2vcFhNOgnJ0bN4MhdxRvgvjpvxC4PXczbk97e0WyS7RRRxASCWW6umkWYY3glVi5HoTBg9ojrQbBJx64VSmq8qRAnfAAOb1VbsZao8YtkQjEEwNlUBNrt+qBpJdwgTKNPpeJdH9a3uPIznMJ4Qsm8dtGSg5krEuwcG9yChBggaTDKN1LTBaCfk09pcNd6pXfji0qqxk1O5hCmED0ghbKAwDr1gHw8jKPxfLPWvtQrrOP4o2JHps63OAi4vUjZTifPx/RXBuuJ2nB9tUW1tSQwySRgfVUcKPPrNlk5rtTE5UgiN6ILKeujthP4kNDSAnddKms8PuZxGDvuDMJ5Usuvy6oXFtiGfxy6pIp/Pw+To6echVIF9xR047ddgrfx3LNcAzks/Jbn7RXy3fAi7thFi9S/7OTHZDKSTmFQSUhOYunYYG8Dp2oHp3gXde/TCXjODfO9hzLiOw+RSsOV/Y824Ert1PhIII75yjO3Dcnqwlt9I5hc/QOZdwaWJQW688UaefPLJU7bdOxTn//74cf7vjx/H5/0Yb73sEhbPmk40HKQ0HCo8B4mGg9TW1hKdtRhiTecUrGDNu+7U/cznIDGEGRvETIy84vtfN+TzcHg7TuaEJZtEypDyevCFzvweXwgJluhj/rUAmEwK07UXkxo4fXnjYA5sgmwGKYkh0xci4bLTlzu+fW8AzkaaiyiiiFeFItktoogLhUiTxgfnJnFyaSxfKc7QHqhaACN7kNh8ANUM1s6Hrk1QOR1G9mGiM8/ZsktENNXMyWnakyeEJOMY2wvt8+DgQaishOE+TFULsm89tKaQ0laMvwGOPqUEL5XSKf1ILVN3/PmMNiPlkqqVFZ/qEN1BcPm1Yc7lBwzkM4VQi+PjQi2qnKxOER8nyPmCB+/xwAJT+L9xwMlhujdjai9FYpdMVZTFckGwHDOeQny9iMuNcXs1Ma26HsmnMHah+clrQ94NAS94K2B4QNc9MgJlFWpfdnQ7+Eu1Qj3Ur2PCo1pKl1ftx5wM+XQCRGUHZ6uBWKW1mHd9Hmftf2LtewFvuovsE/eTLp2GeDyI2wM+P+L1Iz59YLvAspQ4ilVIKLPU/7a0Himtx8SPQfdOHJcP6TuC5bGwgh6kugXq2jGZJM7OleQ2HoN0Gimrwp69pJDOdwTXkreQ2/oitifHZ+/59Glk92Sk0hmeWLuJJ9ZuOusyjVXlzG+p5dLWBubPmsGlS5cxbe4iLF+ocKPlP2UG4JTPqO2C0iqk9OLy2zXGQGIYM9QFieEzLzQxgjMxqpHXrQuhrBbx+JCW+WdfcdsSXf9IH86eDaol9waxWhdArKHYoFbEeaBox/BqUCS7RRRxgSAuP8ZkoXQ6DO2AqkVIaTOMd4PtxWTGtYsekJJazPBhrTa6QzC0E+c30ppEBPwxCFSeToRtD8rKAEvUm9b2I7kk5u0fg5XfgIpK6DuIqWlD9m/GtDhQUgflczAD+xF7Eo5ugNbl2hhnFXSZtkcDKwyaspZLqj44m9RI20zi1DHaHpUx2F5NfLM8mML4xMlqB/9J3sGnwDiYkQPQtx0zegSq5mkksgjSvhyz7XGIhtRRwTsCmUn9vnZ5tGqbSWmFbrAbYlEY7IWyKug7qrKEaFTJcDwObcth7xrIZDSAYnISPDlt8rMsJcjHdumw8mewmDrlXHuwr/kg+frZyHM/xD12FHKWalqzDmYyj8nnleAfd5SItWDKG9Qb2clrNdw5+ZhYUHUJZqwfp2cXOV9Mz8OBpyHxY8Trxo6WYc1swp5+PWYiSe6lp9SpoqQMu6VJK+aNc7hq+Ch33HEH3//+91/5g3sWdPYN0dk3xGPPby+88s9EQwH++PYVfOnjdxL1uwr+ur9fkKAXKW3Ue7E9z6p3s9uH1M3UxLkzoSSmDWheL9acpQCY1ASmczdmx9Mq7ambAbWteiNXxPnB7dMY6jcDinHBrwrFv6oiiriAkHATTA5gsuM4+SxWsAqn+3movgwGt0LlohNVnqYrMHt+gbTfgITrT7vnNiYPyX7M4HYMBvHHIFCt1bRQFaSHIJ9GMkYrVu4IZOPI0Q1w3Ycxa76rEoDBI5hoPbJ/E8zyQuk0JDOuKWJWGg6txVRMA7dPq64F2zNA7bssl0oOLBvEBTgn/c5zotEmn9YGOyePODmMkz2H1iQHCdVgSlrg2EZM53qIdkFZO9KyCPPij8FTqhXnUAgSY5ixEY18dWcgVRiryYC/EiJZMG5NVUuNQ3c3NDdDwoJEv/JtVyFIYnQCfLbecBjR9QweUA3vyDFMdlKbCl8GdusVmFgLzjPfhqP7YLhbPwf8Zg3FwNABrfD6wxAoQZrmwbzrtFL6m+feWYHZsRJCFVgtl6l0ZXSYXMdu0qseQZ5dg+edH8K97FY9ioM95F94HNeyW8k+9XNc5SG+c9/Xuf766/nBD37AU089RSbz8gT+XDAynuRr33mIn/zqWf7jP/6Dm2+++bde58UG4+Qhm0Kyk0hFkwadpBLQ23HmpjYDZv/z2gAngkSroKJOk9dKS5DSEl3ncA/m8CadNQiWnncQxZsalc1I6+Vv9CiKuIhRJLtFFHEBISd9Pn8AACAASURBVJ4wzthhKClUdysXIKFaSA5ApBni+yDapstaFjQtwRx4CmIzC9pWz0nPHiRYgwRrtJt/cgAztBODA8EKGO9SGcDxqeSSOsgNgUlC3zZkye2YTY8XEsLGNYVr11qYH0LKajFWEAb3Q8AFPTsx+TwSKIForVab3R7IpiA9CrmTfGELcao6DV+IAz6lKu0UJLwFHa/jnKFZ6/gBA1PRDsSh9nJIHIOhvZhsWm3ZfCWY0QTizyOBKMYzCBMTUF6F4MO4UqrnDYdVq1vig6ExCEU0US45AuPjqtkd6ICyehjtg4E+MBY4Lkx6AjFgRCDeB5XTIJWEIy/g5NJIxXRtVjtLVUQilVg33g2HX9BK7m/CODjJUeg/ovZcqQlIj2OGjsKmx8k3zUfe9mGsk0IZxLKReW/HdO/C2fQoMv9GrNJyPAuW4VmwjNyeF8h8/1/JlFTivfH92HXTyEeqMQMHseqbMd4A9o7VfOADH+ADH/gAiUSClStX8thjj/HEE08wMHAG7el54NixY6xYsYL3v//9fPWrX6W0tPSV3/Q7ArFstaU7yTNZABoXvOJ7TT4H/YcxR3fjZJIgNlLdgtTPQVqu0GWMgZE+yP32Nx9vGgTfRA1qxcruq0KR7BZRxAWGhBshPYLJjOE4eSTSiOnZgFV3BWZyEJMaQnzasi3Bcqi7VBuiMkklkvnM1LPj5NScv6wJShuwAlVa5RvYWpgiT2uTl78U4wlo5TXaqFXE8S5kznLMvo2QGoVgWAnutlVwxR0QGIPYDMxwL9J2C+IPwuH1mJ6DiMsDFS2AUW1vOqHjcrJanRRNGgNzQoM6pUUtVILFVoul4zIH6wxftLksHN0IoQqtTFsCLddD5xpM70vQfjXsWgVts8HlA58XshnM+BgSCIPHoxIFfwRSI9AwE3IdkM5DclzlDj09MGMG9A1CzQwYPgrZLFRWw8igjtcbBCZUJtF+PWx4CHnrR1Xa0H8As2elTmHXzUN8p194xeWG6Vee9TNxXNlq8llMvAeObMN074WBTjiyFfOtu8nPWoZ11R+eYkMmdbMhWot58RFoW4ZEawFwtS/GnrkI57n/JPvY10hFmvG+5R3kNzyC68YPkH3yh0htFWZsEIlUEA6Hue2227jtttvI5/Ns3LiR9evXMzg4yMjIyCmPoaEhOjo6cM52g3ISvvOd77Bq1Sruv/9+brzxxldc/vcdYrugZjpSMx0okN++Q5itq3DSk+rhW9Oqzhu/T4lzrzeKNm1FvAKKZLeIIi4wxFuCk+iEklYY2qm2Y74oZnIIooW4YHdEG9UACVZopfZM66JwwRw5gjm0RqdAA2WaIIYo2QVMsBxJDmGyqL9sxSwY3AkRN1I/A3OsQ5vD/G4YSWBeeBi58gNgdSK+Gujfqt6vbh/SvBBSCRjuUGmDP6L7Im6YHIcxjUbFYysBteypi5FxMkqMM0klxlLwWRVOODlMPSzEGChvgYkhjTWumQepIX2tbwdMmwkv/QxCNTqe0ipIpTW5KxhB/EHM5KRKFgJeGOxBvDZmPAN+vzbTDXWqPleAvr2F6eOCbCGTLjT6eZTA57NQPRMmJ/T4iwVVM5GqmZj0OHRvw8kkkdZlr0pDKLYbKW+C8iZYuAJnbBDz1DdhqAd2PYOzbz1cegPWwrefaNYLlMLl78bs+jVmqBNrulYIxbKwr/4zpG0vzvM/Jv3z7+G5fBnOnrW4Fl5NvqcDtqzEvvqOU8Zg2zZLly5l6dKlZx3n5OQkO3bsYMuWLbz00ktTz6lU6rRlu7u7uemmm1ixYgUzZ84kFotRWVlJLBYjFosRCoXesAYtESEajVJRUfGqPYZ/q+3bLqidqeSWwt9y70HM9qfOPAtQxJlROx1pnPtGj+LCoNif9qpwQf+64/E4n/vc51i7di3RaJRPfvKTrFix4rTlvv3tb/Pggw8yMjJCIBDgpptu4p577nlDvoyKKOL1gITqIZvApIe1uhudjunZgNQtRcrnYoa2Q2zBOZEAsV1Q0YpUtAJgJobg4CqVGRgHEK1gDg8i05ZiOtZBtEKbvfq2QWkzlFdCz2EobVJZQ9chzLrvIFfdieWLQLVuy4wPYw5vVvIYalET/kQexvsgHEPKW6C5GrEsTD6rBDk9UZian4C8R8m4y3PiYXuU3GZTukwmiUlPFCy4xmHkCNI6F7Jj0P0ShCugch64PMjgVozbjxkeRiwLCVVipFPtzVJJbQqybUhOQKwGRuOYUB2EjSatDfbqcertVSuyxAg0zFV3hqEBrUQ7jlar3W51aUif2SpLvCFouVIlCPufhth0JDb9t/qcWJEKuPUzOIc2Yl58XJuhXvgZzo7VSPtVsPgWLNul+z73bZiunTg7V2PNOWH1ZVW1wVs/hPuXD5Dr6sFO9eC66UrM9vVQU4kZ6ERijec1Lr/fz+LFi1m8ePHUa11dXXzkIx/hF7/4xRnf89hjj726g3CBUF5eTlVVFZWVlVRWVhIKndl6zOv10tLSQltbG21tbTQ3N+N2vzb6WrFdUNeG1LW9JusroogiFBeUPX7pS1/C7Xazdu1adu/ezV133UV7ezszZsw4ZbnrrruO2267jUgkQjwe5+Mf/zgPPvggd95554UcbhFFvG4QXxQncRTCzTC8G6mYC9FWGN6HlLdBsBbGOqCk5fzXHSxX6zFfyZTTgWQ1qcnyl0Lb9ZiDT6vOtnoB9G5BypqVnPYeQVoWYtwB6NiBWXM/zhV/hhXWlCgJlSFz/0B1hck4+EvO6rMqthsCpfo4/tq57sNJPzs7V2OO7ENmLIWSJujaAIeegZJKmBiA6VfAkRdh3uWQGICKGhjohmM90Nyq+tzJlBJuyYG7HEkNY4wLvG4ItsLRHUpsJ5MaRFFwScDr1hsGT0AJeT6vscHGkO/biVXWPOWPPDV2bwiZ/XZMzw6cPatedZX3ZFgtl2Ma5uNs+il0bINsFrN9Nex+jnxFI3LFbVixRqR+Dojg7HoKa/a1J94frsIOenA8XiifQ37Dz3EvewfZF36NK7UB9q5XqUWsSTWkwfPX2NbX1/Pzn/+cb33rW9x9990kEolXftNFhKGhIYaGhti1a9d5vc/lcvH/2HvvIMnO68rzd196W1mZ5X1VV7X3BkDDEg2AIAlCAEmJ0pArSkFSnInZnQnFhBSr4MYupQmtFLOxOwpptJoJcUcjURRFkSBIkAQJS7iGbd9oV93V5au6vMmq9Jnv7h9ftil0FRposhuNxjsRLzIrzcvvmco8737nnrNq1Sq6uroIhZaP+o3FYnR2dtLV1UVXVxerVq3C53NkCg6uAo5m96pw3chuOp3mmWee4Sc/+QmhUIidO3eyZ88ennjiCf7gD/5gyWtbWi5WGVQVy7IYGBi4XkN14OC6QMKNaDEDmSlsu4QVqkUXRowFWajONJvl5hFfxftfuTsAgUqY7wNVtJi++LkuN3Tdhw4fgunThvCOHzFG+sUc2ncQWXunacgaPIu++S1sl9tYeIXjRioQqjK63cUkdqlofENTM5BJliOMy1IEcZVjZMOm8imWkTDk04ZcXwl2EanpLI/rAFbbdtj4r9CRt2D8CAQroLYeTiTBXw2z55DWu9G5x8GdQwt5xBtAPW5ILUJVI/QfgLa1wChQDVPDJm1tfBxCIUiOwvmx2gVDcEsFsz12yWh6194Fz/09dkUldGxCwlVGXx1pupAIJg0bIdH2K6vyiseH67bfQtfchb3vMaOVTs4bzedTf0MpXAktG7G2fBxRvYzwSudOPPNz5E734A4WwVIjm1h3J1a8xgRaTA6g3W9ip5PlN5Ubss47RASjEIxCtNqQ43eOUYSvfOUrPPDAA3z1q1/l2Wef/aW2+cOAYrFId3c33d3d7/k9IkJLS8sF8nvp0t7ejtfrvYYjduDgo4frRnb7+/uxLIv29vYLj61du5Z9+/Yt+/qf/OQnfOMb3yCVSlFZWckf/dEfXa+hOnBwXSCBBDp5FCLNxoUhvg5qNqOjb0Lj7RBfh44fgJrtK5rzr4hYC+SSpnpb9joVbxDNpxBvyPjUNm/Hnh2G8UNGFjB1HKKVkEqiJ16CjfcYsmmHTaUzXmdiiccGwHUO9XiNVZddNFVR1EQZmxi3cjhEAfIpY5GmdrkqYQFSfg9LHwMjGXB7TXqZJ4ouTEC4Ago57KFjWHYJq3k39thhsLxIstfEJM/PI+FKmDoJDV3QexQGB2FVpyGxExNQzAJ5NNqBJIeNbZrXDZWNMNEP9fWG9HbthpN7IZcFUTSfQVSNgcTCDK49v41u+jj2kafg7X2oz4+u2ojEZtFSDqnbivijhuCvexDOHcc+8fSKdlLickO0HmINiHf56uCF11Y2Yt3zVfTAD9D2zcZj+PUfwngfpOawR89g3f0FBLBPvoi17mPmfR270af+Ev8nf5fsM99DXn0cz57fIffjv8P7iS9gRWLQ0IU0LJ1p01LRyFXSSTSTNJ/T/Ya5yBELqWpGGrqWpIK1tLTw9NNP881vfpM//MM/JJlMvvdz9yMAVWVgYICBgQGee+65Jc+5XC5aWloIBJa3tfN6vYRCIUKhEMFg8MJ9v9//kQ2nCIVCfOITn+Cuu+76oIdyHeCIdq8G17WyG4lEljwWiURIpVLLvv7hhx/m4Ycfpr+/nx/96EckEr98TrsDBzcaJFRnUsbmhkxKmuVeKmdIrEcnDkK4wVRr3UETznCFHzWJt6O9z5s/SjlwedBwLbIwbpq7yrAqm7ADMRj4BVSvh/QERAJmyv/Ey7DuFhjvRi2Bvv0QroaN92PNj6DpGUN2QnE0GDVVwFLGEEqRsveuB1x+8PiN5y6CuDyouMrboIYE28UyQVZzv5g1DXPZJEydMCliPi+kU9jjPVhqQ1UXzA2YaNWGNdDzFmy7HcbOIpt+C53og9yUqSAHKyCYhskJqGuCQz+CzffD6AGTEDfRb8h3MmlIul4yBV8omnGcv+DI51BVxB/GdeuvozsewT79KnS/jtpHYNVGcJ80DhN1m00TW8NGU+ldAVrMQXIMho9iF0wVXlxeqFuPhC//7hNfCHb+Bhx4DLCRL/xHE3n7xP8No6exf/H3yMZ7kUjVBcIrLg9E4lBYxN22nlLvG1iz5/A9/Lvkn/oO7h334GruuvyzXG4IV0K48nKvZ9uGqSH07AHsxVnEH8ba+SnzPhG+9rWv8du//du8+OKLjIyMMDk5ycTEBJOTkxfu55ZparteKBaLTM/MMDOzQlraB4BSqURfX98HPYwPHf7sz/6Mb3/723zxi1/8oIfi4AbEdSO7wWCQxcXFJY8tLi6uqHE6j7a2Nrq6uviTP/kT/vqv//paDtGBg+uPQDVMvQ3BBnS2B4mvMQT4vJzBG4aqTVBYNCllmSkoZcvBWmqIVKQZ8S3VWIo/ghay5ZjbMpH0h2GiewnZBbD8YbTzk+jppyDRhDTuQAuvGD/a3pOw5WNYpTzavIjOjsIb/4htuaB1LeSz6GAvLCaNjvS8owKYivJ5L13zAFiWkUcgF24vVHbdHmO35PEZyzCPDzxepKbdNGaF/ZCZRVJ57OkBpKYLJrvNFHtVHAbfNgETkQQMv4G0bkEXX4H+PqS9C7UwlmKWBZRQT4WJGSYPWjT2ZuPj0NoCw70Qb4SRbjPuYs7IQihbrBUWwGssxsTtwbX+Y+i6e9ChY+i+J9DxAXT7p5CBV6BqDRKue9fTQNw+Yx8Xb71AKLWYg8GD6Lk8tO82spFL3+MNwM7PowceQ4/+BGvTp5HP/x+U3vwRHPgZqraJg25eg33yJax19yAb96CHnsF739dI9xyluPdxfJ/7D3gf+QqFl3+MPT6MZ+e9lw9wpXFbFtS0IjWtANj9R7HffgFr08V1BAKBG952rFAoXCDf55dcLrfsa2dmZi7IFrq7u39pT2IHvzr85V/+5c1Pdh3N7lXhupHdtrY2SqUS/f39tLW1AXDq1Ck6O6+sYysWiwwODl7jETpwcP0hIhCsQVGYPYMdqsfyRaFmCzr6FjTuRtx+Y+F16fvKt1oqwOIQ9nyfifSNtprb8/DFTJXUG0ZKWbSYW3aiSlwe6NiD9r4MdZ1I0zY09yZkZtF9T0JNKzSvx2ppgvY7sNMpOPUyBGLQ3IlUVCNenyGNdumiVvd8+IW4jC9rsRwNbBeNNMIumr+LBVPJzWfMkstANgNTo2gxhbStNylmLRvQvv0wPWikBb4wFPKIx0ItF5rKIIEYTA/A2kdg+DgsDqF2AeI1UFQY7of6Rtj3Pbjls9D/UtlH14Jkybg3LE7Dli0werpccbbN42W5hp0cw1W11E9XRJCWTWj9auy9/wQv/hO6/X5ITaOz/Uj9NkNq3+u54fZBx240M4+efgFijVC/YUlVXzy+i4T3yBOw+ddw3foopdoO+Nl/AbuELkwjLevQ4eNI43o0t4AW8/gf+V2y3/oziqcP4l6zA+89j1A8eYDcU9/B+8BSP9/3CqttM/bhZ9GhE0jz+vf9/g8KHo+HhoYGGhoa3vd7Z2dnOX36NIODg5RKpcuet22b4eFhzpw5c2EZHR39VQzbwTuwkvTDgYPrWtl94IEH+Ku/+iv+9E//lJMnT/L888/z3e9+97LXfv/732fPnj0kEgl6enr427/9W+68887rNVQHDq4vgrWmuhtfD7Mn0ZodRs4Q64CZ05BY2YZIXB6o6DAy2UIakgPYxQwSbQVfxJDRxaGyZ+y8seYa2gdNOy+TQogvBM070bHjSNutoGIiaeMtaEUj0v0mdj6HxGuR2lZkzVZDasUFUoCSXiS5dslUo7M5E36hKwcQiMtjXA98HuCddk+CDp1BB7qRjs0wNYB03Q4Dh9HRk3DXF+D0z4xbQqwKTr0G2+4xsoXRg0jTOnRh1lR3V61GKZnqbqASZBwtlhB/FA2mYWYCfH4YHoFo1DSuuVxlYm4bQpydN/tyug+qVi+/PR4frnu/jH3mDXT/z9CmdmTrwzB+FNu+nAxd9n7LDb6ocdMIJpBABbL+QXSqFz3xc2jZhZTdMaAcWLHzN9CDj6NHfmgIb9tmSl/6v+Cf/jcoFdHePBqJ4WragLSuR0/txdq4B9ctH6fw0uNofhF312Zc7R1INETuB/8v3nsfQSIrNEdaHjOrYLnKASEXHTlky/3Ye/8FK1KFxGquuL0fdlRWVnLrrbdy6623vuf3pFIpenp6lhDg88v4+Pg1HO3NC6/Xy9e//vUPehjXHsI1rOxem9XeCLiu1mPf+MY3+PrXv87tt99OLBbjj//4j+nq6mL//v383u/9HocOHQLg4MGD/MVf/AXpdJp4PM6DDz7I7//+71/PoTpwcN0gIhCoAhR1+dFkL1R0IeE6dPESOcOV1uMJQnytcV+YOgrxNlicMtVUsVC7iFW7CZ0bRs++AB13G2J16TrC1SZh7dwJrOYdlML18PYTcOIE6vJBRT1azCInXzXE7Z1furZtKrYi5aS0801o1lKHhvP3tTxdX1x+yphS0aSiVdSgvUeRrZ+AnpehZSfMnoNUBlyWiVatq4OTRw1RDNeYsIj2eyByAmb60WIRatug1At9J6C+AQ49ATseNSEZ0zbEGmCiD5oaYbQPAmHjvVssGFmF5TIV6LmxKx4Pq+s2tGEN9i/+O/r8N2H7J0zVeTl4AuANmwU1zYXZeXT6DISqIbEaqeowUoeBfejkGSNtKO9/cblhx+fQw0+gBx+HzQ/hCldS+upfGcKbS0FqBk3PI113YT//Tdi4B++OeylYSuHlpyjufwnvo7+LFYvhvf8RCnufWTpGtwcJhZFQBHGJ0ZorYHkBD4KNVDfjaurEuv3XsV/4Ftbd/8rILRwsQSgUYsuWLWzZsuWy55LJJCMjI8sm1KkquVyOVCpFKpUinU5fuL+S7OKjgHA4zJ49e+joeP9WjQ4+GriuZDcWi/E3f/M3lz2+c+fOC0QX4M///M+v57AcOPjgEapHp46Y6u74AaMnDdUaOcPwaxCIg7/SyBI8wXdtUBMRo5KNd8L4CRDbVFrLxFZiTeCLoKefg467Luv+l0QHmj2MTp7BVd0Fu/+1iSAeOYiOHYP5YdSXQLxho7stZo3jQiFtPsvrKrs0FMDmotOCjamQnpcFqA2iZlzuMvnFWlpdKJVgdthcDIQi6IEnkDu/DMd/jEQSaPdLSHs7TPcjFXUoiubUSAC8Xhg7jtS2o+lF6DuDrNmEui1ILSLRJnRqDM3MIR4fGoqZ7UBgfh5cAqEy2c3nTUKaCBSLkF5AU6PgTyCulaUJEqrEeug/YO9/An39x+hK0gC3F7x+JBpDI5UQqjDSvHIzHwMvQ3wVEm2C9tvQmQHoeQntvOci4bVcsO1R9OTz6OEnYP0DuKJ18KX/ROmx/xMmh7DPvoVr0wPgD2LPjGLFG/Buvw/v9vsoHHyZ/Lf+Ejq24/vYQ/h+7WtLhqj5HJpKootzUCwgYpnmwlIKSllE3BT3PwP2IlZNC7LrQexXvot175fev5vIRxjRaJRo9PLIaQcOAMeM4SrhRJI5cHADQERM5G5yACra0PQY6g0jnhA032UIZW4WkgNoIV1u+VJTzQ03gC92uSzB5UXtkiGSakMpj9pFxHIjgQro3IP2vgT1W5DI0ulmadxq4od9ESRaZ/SoTTugaQf2wjgMvgalpJnK9ntNnLEvhoTiZvodNX66hcyFRd8hZZDzU+AujyG6xRwUska/fB52EW3YBqdfgukixFvRl/4bctfvoqefh5lRqPsNmDprSHQoBMeeRbbtQcPVkJqHxHoY64HJKTSTgpoWyJxGew9BTSOcfBE6d0JdCbqPQ7QKZmZMpVjVEPdiCdJJxC6PrlgyLhMLQ9jlSGbxhMCfAM/S+FuxLFy3fAbd9qkLNnBLoDbMj2NP9qPTIzDUC7kM6g+jiUakRqFoo7MD6EwvUrsRibeacfS8jHbefZHwioWsfwC7fx96/GlYdbtp5Hv0f4X/+nsw3o9uVGTt7fD2c3DPly4Mw7P9btyrN5N/6h/JPf19FAsJhBCfH/H6EJ8ffH7E6y/LO8oXUTZge7HVxtp0N4U3X8B7/yOIW6C9C/uVv8fafPd7/2dw4OD9wleBBG9+yYyDq4dDdh04uEEg3giaGgNPGHTMVA6jHYYUegJmCTcsufjWfApS59DZHpQy4Qo3mHUUFgEBTwiKaTNFnk+C3/ihitsLXffDwGtodh6pfoftVPudaPez4PEvmX63IrWw4TPvf/uu4nWqivS9iq69Dx08CvPD4A2ie/8RaWyDYASdGDLjS09DQyucOYVGWo3l2uIEzPQhFbVofZnMbtkFgSDMzyGdt6JT59BiwUzNe3zg98N8qWxFNm8GZNuGvHvKU/KlnNH41nVhWS6TKFdMQ3YaTQ4Y27FIs2kuPL9dnndpTqvpwFVzcQpWiwWYHcE+/iJ6ehypXQUNNTA/hA68ioaqkKbbDBk/+wq66q4lBNtq24UGYuiZV9HMHK7WXZR8IZgYgPEepHY99qFn0FJxSSOahGN4P/NvKb74GFLXitW+sbztWTSXRXM5NJc19myWhbjdpnJvWYjlojQ6gF10kXv6Cfy/+b9gRVuwSz50fApr7e73eAY4cOBgZVxDN4abuLTrkF0HDm4kxFaV9bbrYPo4pEZN6MQKEG8IvJ0X3Rnyi7A4asIfLI9p1PKEIDdjAh9ycxfILpQrym13oKNH0clupHrN0ue67oWh/dj5FBKohJq1ppHtOkFEoONOGD4EjWvQiSC4LTh3Ap0ahmgtnHwGtj0AA68h1Z1oz0n09GtYdbWoLwruMATjyNwYGquAgbPQ1AKLSXTgENS2GNuyhg5oaoOBM0ZWMDVlKsWucmXcLpnHEfD5UAGOPmUuMupWQ80qJNKCRDDJeAuD2KU8EqyGQM37Mvw30b1tWPf8Dtq9F52fgFP7kXgDtN0Jg3vRvheR9o8ByxNeqe2CQBQ99nPs9BxsuR8OPIk9fBxXXRfUt6M9+5A1S0mouFx47vtNSqcPUtr3tGnoWwH6jr9d0Tjez36ZzON/R/q//keCX/s6Vtcu7FOvUXr1+0veKIGwqaL7PkhNryDldDgCkRWjrx04cPDhhkN2HTi4gWB8c1sgPQaBKrSUg8yk0YbKlX+IxRuG+GpjW5afh8o2mB00EgHLgxbSy1uPNWxG+19DAxNI+OJ0oLg80LbbuD2kZ+HcUex82lRSEx1GXpGeg+ycaTR7t7G5fRcr1J6AId/l+1ciGdK0DSbPmNjg2SnY+DAc/D5EqkyDWqgePH7TkFddC28/C6v/d1P5nO0FiUAwBmrByAhk85CohalxpHkz6h4BdwAJZdGSbcIX5svxwZbLyBZKRUN2PW6gAMkRCPgBQacH0JFjiNuHtO9EojVQudp8fmbCRD+f9xV+H5BgDdbau9DZEbT7VTOuAz+HeAOamYfzhFeB3ldh1VLXGonWwvbPoQd/CLWdZhsWZ9BcGum6E331e7Bm+Yqra/V2XKu3v6/xlo6/gX3mEMEv/M/kXnyC1Df/HO9tD+DZthvrEiKuqia4JDkJ+Q8uVELVRmfOwfApyCSx9VL6vsKxUhup60BaN717td6Bg2sCR7R7NXDIrgMHNxjEH0fT42YqfuYk6oshqXNLtazmlSbY4B0aUfOUaR6Sikb03HFjDQZLdLuXoXU3evpZaL8T8QYvH1ewcinxnekzhDVYCYl2LI//8nWWoaomjOFSHW92zPxdzK5oTSYING1H/BEjs/CFDame6oOiDZSQSBXa8xYSa4aps0jzWnT6Fewz+5DquKlwV7YaUr34KhqLGl1sRycI6NgpCFeg00NIJAJVdbA4Zwik22XcIJI5k6Tmj0JmDgo5rDX3mW0rFWD+HDI3jJ1bhCM/RZu2YLXvKPso1yLB2hX3zbvus4UhNHMKYl3IzkfRt59FWtZCKIF2v44OnEELOazVDwKKnt0LHXcsrfD6I7Djc/Dmvg7GwQAAIABJREFUt9FAFM71o30HsNbeRcltofOTSEX1iuN4P3BtuI3Cy48jY/34PvYIks+g6SSZf/lbrGgMq7EVV2M7VqLGVHYDV3YZuZa4mp92VRvOnUX3P4ldLCDRBNKxfUlcsgMHDm4sOGTXgYMbEbEumDmBxNehMyeNlMHlB7cPLK9xXFDbyBIWh1CXHwIJREzXu2lOKxjtrl0AtwAWRJrQySNQtcnE0V4CEYFV96BnX4Su+9+1g16ClYbkvkeIiBm72wdc/r6VSIeWCujZlyDRgSQ6kGg9lieA3fsqhBIwNYxWNsL4KWh6CKZ7weWG2mY4/FP47NfNds+eQlwVaMsu6HsdwmEYGYWmVhgeNBG/871QUW0a046dMzHE+YKRMQAU8mafW5YJvTg/dpcH4i0Qb8EF6OwQ9skXKM0MYW351GXJZ+9rn0Vb0EIKnX4bibZhbXsIHT6GDh3Buu1zaCaJvvz/YZ89htz2BaSyGT31LKz+2JLjK74QisCOT8Brj6ELU+bxjm3Ye7+DdN6KVDVBrO6qwiQuhfvORyn8/H/guffzeO77DfJP/HcCj/xPIBal0QGKJw5iz0xyQQTxHmYsrhlUyzZ5LuR8aIjlMsd8BdmJWBb4Aoi/DvEHoZCFt542F3DL/s8o4g9g+QJQ04LUtCGhFSzoHDi4EpwEtauCQ3YdOLgBIZbbhE3kZpHK1eWAhmnTGGXny3HBADbiCaGuACyeM6zRn4BglXF2KOXKbgdAIY0UU1C1CZ16GxIbljRQQVlq0LwL7X8V6fjgO+jF5UFW34+OHEH7XoO225BADKvrXuyxPpiehIY1SCEH6bRxhZgbQiqq0ZlJ9PgrSEurSRJrWA8jhxB/GM3NgLqNxVgkAouzEIpAahYJBtFg2NieLSyY50XKjgwzZRcCG/vgDyHRhjRuWEJopbIZa+fnsI89hf3Gd7E2PYhUvP/K7oX1eUJQtQXme9HMFDRuQGIN6P7Hkc0PIp/8A+zjT6BHfoy6osi63eiJZ2DVHeai5DzadhrvYbVNtPP0oEnKy0yg+Qn01CkTy3ze39XlNqEQVS1Q2WDS6jwhcPve3frOsvDc91sUnv0Onk99Ge+Dv0Xup/+Ae+12rPpWXHc+eENpY1X1orNEqQS2jZaKXK5ILqNUMs162TSazZrwlXA9msuU47Evhz0zhRYXsM5N4Y6dRsq/vGK5oKoJqWmDiven63bgwMF7h0N2HTi4QSHBWuypY0igGvFGlj53yX0tLCLpCTS/YCqP6UnwxswPeG4WQnFTdcolIRsy6WrVm9FJ0wgnnuA7PjcOFU3o6FGkYfN12NIrQxq3oAvj6KlnYJXxBpaadnR6FBbHUcsPw8dMylpqEiqqoLoRul9Gu34fiShMHEKqukw4RuYV4/E7MwMtLTA7DVUNaGraTK/XNZhGtny+HIxR9tfNLSKWu+wvPA3TKXSyG3UFkbo1UNuFWBbiDWFt+wx6di967Gm0YT1W+86r334R07yYm0enjiCxTmTnZ9BDP0Vat2FteAQ99STqC6M9B5FEAzp0EOKtSLWJZJemrXD2dTScgL4jaCCIte3TuNY/suxn2vkMTPbDeC86cBIt5aFUMP7FgTASTkD9eiTWcFk1WPwh3Ld9kuJLP8Cz5/P4HvkK9vgQpd4T2PtfKFuwCRIMI+EVUtquC8pjCEWRcBQJRSEQXKIv/lWiNDlmKtsT42BZuFo6cfuj6NAJ9NjLGIItSEWVuUh18J4g8Qak7iMSKOFUdq8KDtl14OAGhlR2odPHIVhjYngtn7l1eS80rIknDBXhSxwZFkxAheWB7CxEakwqmJ1G7RyoIpYHqrea18W6LifTiQ50aD86N2S0sDcAJFILXfcaWUPNWmT1PWj/EZiagNoOSC9AIW+igGf7EI8HrayF/T+FjbcZfXCgApkDKpvQ8V5oXwO9J4w7g22bfZtZQMLRiwEQqobwlopm/X6fqXDWrYO5IUjPo8U5tG8UTj+PVHYim+43nredd6PReuy+/ZSmBxHPcs4DWq6iNkC8GQmuTP7EVwFVm02Vt5SDzQ+gvQcgOY6sfQhO/xwS5c+YmjZuFAuvmbQ1y0L9Udj6AOz9FxMUUsyvKLOwvAFoXGeW8yNVNQ2J81NochxO/AI7NWem7wMJxONHbnkUsSys6ia0vp3i0b24N9+Jq7kTV3Pn0i1PJU3gxwcF20Yzi+jCHPbYoBnPJRKVXxXE7Tba3oo47tUbsCruRr1+7IEz5I8dRbMZxB/AvW4rVksHsjhnYqodvDf4I1d+zc0Ch+xeFRyy68DBDQxx+SCxAYoZI0nIJ8HOQSlf7hxXRNwQSIC/EhGX8etVBcuF2gUkWIvqWSjlkVgnOrYParddQniPQrQN8b1DR9i0w8QKu30Qqr4hpljF7YPVD8DwAVOtjtXC0ClodiEagalRaN0BZ5+DiioTAjF0Bi3eioTr0bH90HiHkSMEQiadLVoNi0kIlsDjN3HIhCAah+Sc+WC325DdUtFU3PxumJoBQhCrA58Lyc9DagbSPdi/OIXs+i2saA1S04UVqUYH9q+oT1W7iI6fQgYOYCOmmc4fRlq3IRV1S/eBWBDrNGRoYQCtroLFDHrk57D5EzB6COYH0dbVyJkjaNtGOPEUrNkDa/cgp54vT9C70IHDyKpb3vv+F4FQJYQqkYYuWGvcHzS/CFM92NND6M//M7L1XiRci9W1heIbP8ce7cVquLzyJqFyNfUmhxbyaHIGTc6YCvfpwybgBHNKSNCL5tPk9z6Jvbh4MbjDwXuCZ+1WvLfd90EPw8ENDIfsOnBwg0PKldzLHi/fql00et7Z09hqG72vqvkVtYsmYtgugCjqDiPVm9CxA1C96YIeVKePQzGDhOovrl8E2u+G8WPo+ElDkESM326kFoKJcrLbIuQXL9yqXTLvt1zgfofVmGWV3RjKrgz5jGmkWwlqI7Xrl9qhiUDzTuxTT8PGT8LIaUhOogULAkUkn4JIPaQmwFaoboODT8MtnwAs0/jXtBPSM2h2EGqr4MQRWNVlyKzlgUwaEgkY7TPyBa8X8rny/RDiBkkk0GAC1EJmzkHaRktBkzLmSqKHv4MdaUe2/hoSiCFr77/isdZCFklNQ2oaXZjEPvQjJNGObLi8YVAst0ndUxu8g9iSgze+g7XrN4zlXP/LaGsLMm203Dp4CKvjNuxMEirr4e0XYcPtVxzTe4F4w9CwFVfDVrR+K/aJl2BDDDl3CKsxTvGNH5hz0uvDilch8SoIhG6IC6jrCbHAVSlQWQVULXlOVU0sdWoBTacvaqcdXBFW9QcphXHwYYBDdh04+JDjQjNbsNbYgtkFEyzh8kIhCZYbvH4o5iE5hMRXQd1OdOIQVLQjgSqkaiO6MIhOH4PKdReIlbjc0LD1IrFWhcwsLIzD5Glw+8EXNkswAd4QVllrqKWiqUiftxrLzJgmIG/QRApH6sETuPD65aBqw+BbaPIc1G9eaqlV1YkUU9jROIyNQdcG47A21gPtu+D0z8DrRVxuNKno1BBS1Wos0KQEsRbIpWFyBOI1MDkBtU2Amma+aA3q9pjGt2jUkLViETw+KCyiTbcgmRmjGfZbF5PpFtNoAYhFIT2C/cJfIFs/jxVvuvKx9Pgh1gixRnMsO3Zjv/0z9NV/wFr/AJK4XFIiYkG0DSvSggarsF/9e2T3byPrHoG+lyBYAE8t2ncYOm6DRKvZ9ld/iIYTaHLC+AL/iiBVzcYbuPst2P3ruERwtd8FgGYWsccH0fFBdGTo/Ab8yj77feN8P5mUQy6CEVNtDkbBs4KLhlim8uoLgNd/QzXbOXDgYHk4ZNeBg5sMYnlQTwi0UG5SmzNNavMpSA1DfJUhyLU7Yfo4Wkgh0VYkUra5mjwMsVWXyxooV1WDcbNcaRwuN7gi4Lt6PZ2IBa23oTP9aM8LxgP4vMY00YF2P4OsuRt9/fvGQzhXBE8IySUh3gazQ6aKXNMOPW9DrBoKOTQ1Ac07kekzqGVBZTWcPg51jZDLor4QkluEQADm5y7q5IpFNL+AFAsw8Koh+9VrsOo2AWVyPvY2eF3ozBQa60S8Y+jxxyipF3Evo9kVgWg91HQhFXVLPI7FG8Ta/jl06BB68jk0Wo+s37OszlbEQqrXYu9KoK/9A+z6HFbnfdhTPTB6wIRJpKZh3f3I6/+AimW29chTpmntMphmKfwhJBCFSxZ5F09lAKlpBbXRN38It37mwkWKBMK42tZD2/r3cvivG9S2IZc2mt1UEp2fNv7Jy8Eumf2Wz5hq/3mP6PLuunzl7/jD7UGicSQSN7fRuNnHH7Eqt4OrhKPZvSo4ZNeBg5sR7gDkF0BckJ0x1VTxmqpsGSICVRvR+T506jgk1htZQ802mD1tbK4qVt0QP8ISb4NQldEQN25DwmWbplgz6g2D1weT4+Dzg7sGho5B+zaTHueyEMmj/kp0uAepbzH7Z+YErLoXTj8Hs2MQT8BQvyG8bp/R9VbGDdkF80NQKEI2CZYXq+MetJCByW7s0TnEXwHVa5D6LVA5j1r7kHQBiEFtLbJwjmXZkCos9KGz3Si2OWZuv5GvhGuh636slu1oog27+xfoW9/D6roDqW5fdl9Z4Wrs3V9C3/pn7HV3INXr0FA1DP83tOd1rC2fNnrv6hY48DOsL//FivvdeDmnIZOETBKd7IfMPHo+9ex8Y11VCwRj74grbjeWb/t+DLt+7YY4j1aCWJZxmAiEoarhmn6WFvLowqzR704MQc+RC/rdpYO6psO4qWC1rsPVueWDHoaDGxgO2XXg4CaEeEJodhpcIbSYQbxhFC8U59H0FBK8qBeUinY0M4WO74dANURbkfhaNDONTh4y8cWoqWjpJcv5v+2ikTcsNw6xyu4Rvou3VtlNYrkUt3fbJl/YNKcNvoUmx6B+E9SuNWS1cQMMHII16yG7AOEGSCch0W7S1jJzSON6tPcQWtuBzPVCw21Iegy8ftRyQTgKszPg8xpy5/GabYSL4QPFgqnm+Tzl/Ry4IPPQzDyMvY1dzCJNu7BW3Wf0zvNjxuKsdqORk7wTqhCKYrkEzS3C3LCRiRSyMN2DzvZix5qh8wGsrZ9Be19Dz76OLk6vaGdmBSLorV/APvBDtJTCqlqPNnagfUfQzQ+ZJj5vDxx4BrtUwlqhGUrEAn/YLJUNl/EvLRVgdhQdPg6pWUOOY/VIy2bjzFDfaQjvG49D2xbwh8y6fKGP7PS/eLxIvBbiV++97OAjDKeye1VwyK4DBzcjfDFI9gNiCJsvBpYAPnTmFBRakIqWCy+XQBUSqDIxxeP7jbtDtA3xRSE9YaqN4jKE9fx96/ytG0usciRwFrJzGDstH2p5TFOblspxwQtGblDKmthfuwhavEgqL4NCoAqpXI1Y7iWyBkYOIU3bTcW3ogX6DpjEs8UZNBhDRkahdQtYg+D2wFwf1HXCmQOwYTfMnjHhG213Qu4ZmJ+CygT09kBVDVrZBvn+iyTX5zOhA/k8BNzYZ55Hmnch/mh5H1ZAy61QyKLD+8y+q9+MVLahrreMk0ZhmQqeKkz1YxcK4PKYII3qrZBoRtPT0P8KzE+gB/4eAjFovQOpqEfPvoGdSSLr7l22air+MNaOR7EPPYlteZG69eiZI+jcMLTuRPrfQi03euRp2P6pqzrNxOWBqlajhaas6Z4bRU+8iBaykGhGmjYi4UqYOYfOj0Nm0UgGVoiI/lDB5YFwpdm+UOUKCWqYi5xA5F1TCR04cHDt4JBdBw5uRnijRivo9kE+iXoj4A9C0Q2lHJoagUIKqVq35G0SrDVWZelJdPwA4q+EaNs7KrklsLNQKEBuHi1mLvb5uAPgqwAsKCwipRwUc6gu9QwVsYxXrct3IQJ52aqCKjrfiw4+iwZrkcQmQwbjbdgzfYYsN2xGel5CozUwOgQtHUg2hcYakeSUifKd6IGijcQq0bkxdKwfCfvQqg0w2424PajHC3ktE1u/SVMTMY9nsxAKQyZrHBtaboGJU+jkWTQYhdqNSK1p7BOPH2m/C10YQ3ueR+o2Yq3aA4vjK5B6hULGuEgU0tilAjrbC0P7jSvE5k+bC5fZPigInHkO9fqg83ZT4T34Y6xtn16WSIk/grX1IezDT6JtXVDThB5+Ete9/wbbF4LadnjjR5SGTiChSmjoQmraTWyw+/2HGogIVDYilY2G+M4MocefN8dJLhmf7+oilG8MlM92tw91ecxF3MwQjJ2FlVLXCjnIpbGXIfiCmGhqf8gsvpD5+4OMUf6wIVqF9UukFH6oIFzDyu61We2NAIfsOnBwE0IsN+oJArax7ypm0EAESaVRXxjSY6j44NxBpH775e8PViPBaqPbnT6xpIp7sbLrgUgL4g5cUY/5y3yHSiCB2iWjaR18DvVGoXY7UrcRzh1DGreCN4R27IJDPwFfGJ0+Z8hCIQdN64xmuZQypLe5CwZPotW3IkMvQdsDsDCF2CV0dgKqEjAyALV14HFBKAizsxCOAGqqu6EoctvvwFQfOnwUTr+I9ryExlug426sQAUSqTOa27Gj6NQZY3fmXr6x69L9dylltdPT6MEnDEne9CkYeQPNeCBbRIZeRdpvQQcPY7/1Pawdn0U8vmX2XwRr4/3o2VfR1s3wxo9NVXXtfZB/GqaHITWHZtMw0W+S0hBwuQ3xiiSgeT1WfWc50va9kTARgUQLkmi58os/ZFBVKOaRsn3ehWUlslvGsv1rakM+C9mU0UcvTJt1rSANcrAM6lfDR4XsOrgqOGTXgYObFe6ACaJATJOa5UbzKaRtNzrjh9ketKIdRt6A+luW1VBKoAoCVZc9fr0hlgtJrEfj69DFYRh+CQ01mCYy24ambXD2FXB7TbBEvAZRUF8EmRqGWD1ke8DjQ7SIRmLQc9gkqM2egYoGmOotV3TThmhksqi/AkIhQ3bPE9KSDSNHkPqNUNOJ1HSiaqNT/dD3Orz6t9hr7sNq3m4IX/0Wk9527siKlV37HcRGXF7wR5FYK9adX8aeH4ODj6PBCmjZBKNH0GwJOXcQqe1Cp4aw3/wu1vZHl01gk0gVdtFGAkHUH8Tu2Yur625szaFf/HNk+ASMnkZnRqHkNjZ1hbzZt+l5GDqBXSq7e0i5ElnVZGQKlXVIrA5itYg//Ks/+DcgRMRY0Hl8ELzcteR9retXNCYHHxUI1+6suXnPRofsOnBws8LlN7ZjrqCxyxILbdqOdj+LdO1BPQEYO4jWbIXR16FuB7JC5fFGgYggkWaINGP3PglVG2HihJEKuH3YNZ0w0YPUtqMzIxAoEz9JQCgGqTmYLSENHWh/N5qcRUpZqNkCoTiiguZyEI/D9DSEg+A9P51f9pYq5NG5oXeMy0KqO6C6A3txBt76FvbwQdj1JSy319iJtdy28na9428t5iCbREcOQCCG1G7EuvffYo+eQE++ANWN4MpAOgvSh0QSqD+Evf8xrG2PIJHLL1AkWgNFhVVb4dgvoOtu4+c7N4S19nZYuzRgws5nYWoQxvvQ+QkjRSiVjP45swBz4+Y5lwf1hyBaAx4f1r2/c1USCAcOHDi4VnDIrgMHNynEF0NT5wxHU9OkJm43dNyJnn4O6bwHbbwdhvdC02703D6oaIVI8w1tE3UBVZtgvg/NFqF2g6nuJqdh7Ay6MA2xagQ3OjeNTA5CTYOZHs4sgvohHoOxEbRjDTLfBzVdsPgGBIKwuAAej9Hvnq94F4rgcZsGtVxmxWFZ4Tjs+X3sk8/CL/4z9rqPI03b3tc+FbcPwtVIuBqdH0Z7nofGHVgN69G6NejRn6PJJBpPIGVrNPFF0YbV2G9+F7nl81jvCIqQ1q1o916kug49nqOUHEPW3gevfBO7783lB+LygNuLREKmau5yo7ksaB6pbkJ9EVicMeR3dgSyaeyX/8kQ3g/DOeTAwYcNjhvDVcEhuw4c3KzwVWBM7P1QWEArKpDsDFLRDqvvQ8/8Amm9DW27D/qeheY70ZleJJ9EI03LhkrcSJBIM5rsNRXbyW6kZi2EoiYKd/AMsnE3Oj8K4UojH8gXTLPa+GkYeAvZ8UU09xMY6YWmNqheZxKxEDSdMqlpmQyEAobw5rLgD0DRODLYh35qfhwq6kyyWaRqiZ7VWvcAdvN2ePNb6PAhdONDK4QxCHgCRrqw3LMVTSZtbuQAOi3QuB1r66exz51Cjz6JNrbC3DQSKiEVAbR9M/raP2Lv+nWsROvF9fhCJggh0Gkqw4eewLrnX6N7/t3FYIRLoaZxjtyCiYLOLRo5hscLwSDgQoo5tCKKuANooQRvvwADR9H9P0V2PfzLHWAHDhw4+BXBIbsOHNykELcfdXlBFEoFEJeRM1CuHK75uCG89RvRrodg4MWyrtePzPahwTgEqhFPcNn1G29dLfvu2uWlfF8sEPeFhrZrUeUTEYi2o8kBdHbKJJk1bcNemIW5MXR+GiI1xgN3fAIp5KFlvbGIyo3B8H4k3IymTqLzE0ggBjWrYfioqe7mssZ9IRoBv9/EBocjZvtKJQh4wR8BXxCd6IOzb5l9EoggXXcgbg9WOIHe++/Ro0/A/u+jrpW+ctV4/QaiEKww63D7DcmNNhqnheZb0PS0Cdao3YBVvxa7shHd+3dQEUfT81DKI6vuQVtT6Fvfw95wP1bLtov7rKoFzVvQvh72PY9m5sx2rwRvwKTvXbrfKR/7hQmY6DZ+wAJiF9GuLXDkJfTEK5TCCVzrbl9+vQ4cOLhKWNfQqePmdQBxyK4DBzcz3AGwC4aM5OdQu4CqGu2r5YLV90PvK0isCVn1KezkEJzbjwYqIbOA1PjQ3MzK65fyF6+4lt63i8aezC6CltALWaq6zPvdZX/SFQixN7pyAEWkGTKT4MrCTC+SWAWRGCSaoe84svVj6OIY1LeaoIipCajqME1XgwfQe/49LEzBeB9UNkJlB4weMzZt6ZQJkBALQhFDdsFUPPN5WBiBdNmZwhOGijhSvdpw/sM/RSvqkFW3mOa6rZ9FF6fM/rgMpoKq6TlIjsHiPEyPoKWi2SUNa0xDWKILCSag8z4YOYimprDqN6H3/Tvsff8M+SJI1kQod9wNkRr07WcozY9gbfikOd5Nm+DtZ5C2LtTlwh56C9fqj7/bGbT8YReBaC1Ea81RXZiA8VPg8aJrd8Hx1+HNH1Ca7sGqboFE64pOFA4c/NLwhxH/1ceSO7j54ZBdBw5uZrj8kJkGyzSrSUWHSUWr3mqaqkRg1d3o4D60mMeqXYtGGtHR/ZAdRUf2Q+NuJBi/JtVZVfsCIV7WtkkV0uMoYqrMrqWNTyIW+CpQVwAdO4YkVmG17cZOzcHMMDozAZUJxC6g8/NIhQvSIajpgMJJ5Oj3kIZd2NlFdLoPCUShshmZHkA9HggEoFiEwHlLr/NJakUINhrtbikDpGExhy6MGau39R+H9AK6/4dQ2wUtm5Hwu7taSOLyx+zJXnj7SbSuE9zD6MRxxBuGmrWwOIHd9wrSejvWri+gPS+i/UcgGIKBtyAQgbt+D177H9iL48j238TyhVEtmVS8ttXQcxRtudVEHf8SkEjNxSr6qix2oQSn34SBU9iBMHIzBEg4uHFR2fQRIruOG8PVwCG7DhzcxBBvFM1MgoCWilj+SiNVmCgT3nIQgbTsQkcOo+eOIfUbkcZb0Ow8OvI6DL2IoqjlBk8QAnETBHH+i1FtyCYhNw/5lKkkw8Vqr8tjKsyeoElgswsmmMEulDv8i4bUuj1mvS4/eAKmEujyIpFGI4fITBpyHKhGXJf4yUbbjRew24U904sV78Cq6cSeHoWBY1D1CXRxBJo70MkRZHYcErWmAjw+ir06apLJpnqgagFq1sFMv9ECl4rGgstT/qosFMHlgpKNntxr9MCJJgQv5HIgOfCH0O6noKoT2fVZ0zD31mNQuwqW0+yqkZloIWt8gQs5E1QARtJw51fg9CvQewAa16OV7TB6EKlsR+o2oj2/QNpux+q6FztSjR57HqVovJJP/Ri2PYyefNlIKXZ9EalbbSrZLVvQnhPo2GHUdbk/75LzyHKZY+IJXTgH3nnhceG1Hj/WfV/FLuRg6CScG4Yd65AVIokdOPilEaz8oEfg4AaHQ3YdOLiZcWmTWtFMw4s3AvG16MRBqNmKWIa0SONW9NwxdOQw0rjVVPs6HoRcEgqLaG4RMjMwfw6KuYufIRb4K0zSWqQe8Zc1oMUcWsiYmNzsvFlPPmVkC+4AuKKG/Lq8hswWMiZOt5A2ry/lwS6gw29CZSsSX43640h2CtUCBGoRl9dIHFwetOV2OP00xDugYTNMnoWpYRjvRRK1kF1EXUHwWsayK9wCi/PIocegbhtaakOnehFfxNiQLc6gApRsxO1GRYx8wes1lV3bgtQiLB5HPX4IRZBQzOyjSBDmhtCFcaR1N3LLr8P00AoyBsBdYZrX3GXvVpfHpLclJ9DjL5hj2HYrjBw1WtnKFkhPQm4B6bgH7X/F6HjrNmJHG9E3/xmd6EYaNsLQQaSqBh04jl0qInWr0UM/QbZ+Eg2HUVcCV/PGdz2N1C6Z41JIm2M0N4BtF5BQNcTaLmuuExFDeH/8/8D4WfSIB6qaDdn3Bcy2egNmWVHHfIPCcoEvYAI3ysfJgYPrBseN4arwIfuWceDAwfuCJwxY5ge6mEVLeUMQPSGo2nSxwlsmK1K/ER0/iQ7uR1p2mh9yfwX4K5D3O0voCSCeAATjV37tu8DOzkP/C2jyHMSaIZhAw81IesxMxwNE25FkLxqpwZ44hlWzEavzLuzpERjsRps3wuxZpKYJ7T+DxBTCHVC3FgYOoZ4iBEIwNgDxKajdBIsvGmVFLoMG/IaUZbMQi0EuD3XtMDlgKr+lkok2zmZBbCgVkaAPwpXo8AHTBNa0c9ngjneDRGuQbQ+hhSzauw/wGC2xPYQGI1AZQsaOQMe9MLIfTc9g1a5HP/ZvsA88hvYfgMbViKf1ex84AAAgAElEQVQCgkH09PNY6x40MbcKrLkF9n6bUrzZnCP+kJE/BCImJS6UQAIR8IXBGzYXAmC0zaqGcJ87bIhvsMoQX7epEovXj3X/V7Cf/C8wNYRW1JSb+wpgpcp6bfnwzZzagJYr/sX8FTLT3oFgtBzAUQeRxPs+Hxw4cHB1cMiuAwc3MUQEdQfKmlggNwtBE6spbj9Ub0UnD0PV5guBElK7Dp08g/a/gbStHIRwvWD5K2Dto9gL52DoVXRxGvIpQ9gKKcQTQtx+bLsEzXfAscfQxGokXA1N62F6FM4ehMY2dOws0r4GHRtCJrqhYT1Eq5HBw2jFKqhsRxdmELfXVB3dbmM/JlJOV1so++4qjB6HcBw6b4GpARjvNxXvQBQyaTQcRrJZSNShxQCcfQm9wlS+IIZY+ivAH4VApWlw8/iRNXcZF4Sx0+jpvbA4DbkFtHkbDLwCrXfA7AB278tI625cOz+P3b8fPfY0Wt9kKpFDR2Hdg0jzJnTwKNJ6JxQXy6nSttEg56YgOQTZNJrPmse1BF6f0QMHQqYym1gFDZuwmm4pE98pGH8buyxjEbcfQjXI7s+iB34Og8cN2bVLRo5yIYL6w8Z2MRcKlmW8h90ecyHk8qxcGfP4Id6ARKvNDMHwCViYLjualBGKIbHaMhGOv+dYZgcfMYhcOzcGp7LrwIGDDy3cfiMlsPyQvUh2oRxLW7MNnTgMlasRn2lUkuou1HKhvXuh/Y4bYqrWitSj6z6HTnfD+FEIVqDuAFLRAYBE22FxCK3bjPa/AB0fx+q8G3usGwa6Yd3dUCyi0yNIZTWaTyOT/aZK+f+z96ZBcp3XmeZzbu77WvsGFPaVBAnupChaki1bUlu0w90ej8cOt+XwTDjssR2W/yg0shWOUPiPuifcHfaE7Wl73NMxHkkhyWp7JGuhKIriCnADsQMF1L5XZlbuyz3z4yQKAFGQRIokCOA+ERlVqLx587s3C5Vvnvue95Seg7APCQ2h556E3KilNqyvmEdXMaFXWbfFqMIH/j1MvAQnv2tiMD8IfXvh5FNQLUEgjFZmoVZBBlrmC5bNfK4meMTxo8GYCcBm18ox8zIk+qB/3+WGwoFd0DuOHv0nWFuGyhOw50PoxJPI6INIvBc9/yQMHMTZchg3PYh+/+8gkwRx6Mwdw+nfBxNHcMYPw+6P/8hzr64LxQWr0K5Mo8VlmH0STj5FJ5lBEllID0J2mzUz+kJmYaksIiFFR7ZCcfmKHerlkcSdzUYov4e5VJ3uuNB2uKo0vdl/E8V+h5bO23lsN01UhCOQzFnmcSpvk/mWT8GF523wyZurGd/WyOgBnD0/daOX8S5xYxvUms0mf/Inf8IzzzxDoVBgbGyMP/iDP+DRRx9lenqaD3zgA0Sjl+MqP/GJT/A7v/M7G4/9zGc+wze+8Q0ikQif+MQn+I3f+I136FiuxhO7Hh63OBKIo40iiKDt+jV/zsQJQN/dsHbKJq5ldpqwyo2jjh89/S3z1SZ6ITlgloZNxK92WlArQq1gb+bBaLepKfq2eRtFBMnvRrM70ONfhOAcGskjwSQSjOMWq9C3H05MoGuncbK7YNdjsDQDr/yrjcqtrlr8WjAGbgl8Mauirk6goTokR9DiMhIJWvUxGLRGtVgYlrtJDI6DTJ5AO21k2yE0OwAzp+HkEzZ0YXQvTJ+0Km2pgFYKMLQdiSY3OygIRtFgxJIz2k3zK7cbgKCBEJz+NqSHoXe3nQNfAA7/Anr6aVidhKNfgoMfRaeeRXp2Ids/YEMoitPI4CF47H9Bn/jfIT8Ep54wu0o4jtbLSDj+o8+749iwjswAsuNee71dF12ehPMvoFMX4dxJSB6BbB/qD0CyxwZtpIdx+vZbs+EtUq1U17VpfM0aNCrQqNr3unmiiLs8DYVZqK6Dv2N2EXGg3rbz1uk2dQbCSGYAtj24eTOjx+Ykf3jSicfbR7vdZmBggH/4h39gcHCQJ598kt///d/na1/72sY2L7zwAn7/tfLyL/7iL7h48SJPPPEEy8vL/Nqv/Rrbtm3jfe973zu+bk/senjc6oRSUJ6yS65uB63MIbGBqzYRcSC7B62voQtHILvLBGRmFMmMWoNSeRGWz1tKw+UHXt6H47fqZbjbFFcvWW5sq4Z23qS3ke4l/UAEBg4gwcjV9zk+dPRhmHoKjeQgkzARmBixYx25B51/BY324OvfQ2d4G5x6GQ59BHFCsHYaDaSQTB8snYLcNmT6KBpuItsfQY/+I9z/72D+DNRrlsIQ7FZlWy0IBNDZk1ZdTvZAoQjBGIzssZSJhQvQM2JjdGsVu8Q9cw69rtgTcF1r1AtGITOEDO5Ckxlk6TiIHy3NwtpFyG+39Yogux5G506jF47Cy1+F8QehumLbDR2GyjJ69tvI2AOoBGxt9YpFkY0dQi+8hOx+5E2+MpdeAwfp3QK9WwBwXRde/IpVahNpKC3D6pzZTbrFKN1MDN4GSJTuFZU+tN22LOXSmgnlZsPGUvsDQBOdWYaLR7zC7pthcAf89O/c6FW8O9zgBrVoNMrv/u7vbvz7scceY3h4mNdff519+/b90Md+5Stf4XOf+xypVIpUKsUv/dIv8eUvf9kTux4eHm8DwZRVnHwhi+xqVdG105DecU21VcIZq/KunkCdwMY24visqpsceEsX0N7KY1RdaJTRC09DehTp3XnV/U5yCDfWDyunTUiEM0g4i7s+iWT3goTQlZPQfw8cfNyaz773X+HuD6G+HPhq6OxZZHArLM2ChKBdhMkXIdqLLp1B/CHzWLbaEAram0GjAX29UFiGwoI1qcWylqIQiZt/c/gglGehE4R4HCoVUGdzAaPajRtr2eXuVg0aJXThFLTa6K6HkGzKKoHNGrp8GpZOw/j7kFAcGdgJ8Rx67Jsw8xoaDMO+n4WL30fyO5Gtj9g5PPBz8OpXITeCHv86zr2/bv7ntwnHceDeX8B94WtIbBS2b4fKMhSn0HrpbXuemx0BSPXB0OWfabsNpRUorkC9YuLXU7s/PvnRG72C25bl5WUuXLjA9u3bN3722GOPISI89NBDfPKTnySbzVIsFllcXGT37t0b2+3evZtvf/vb78o6PbHr4XGLI44P9YUBResFnIHDaHUBXX4F8gcQubppSsSB3D60voouvAjxwcujgN1OdyRw53Kj0Y96fnG6EWMhq1y+Sekr298Py+dwT38L2fqwNUddum/oPvT819GV4zD4oAnz7B509TgM3wXzr6FLL+P0Hcbddie89jS6soDkh9DlCUim0fUi4hNIDyKFC6j64eAvwnP/JwyOmahtNiEWNTHaboM4yN67UfXB6hIsz9hlbF8A4ilI5CGUs8p0swmBBiRTdv9m+KwJjUAInED38ngVLS7BuR+g54Nw6EOIozB4FywcQ898C0buQVJDSCIHhx9HX/26Dbl49u/hrn8L9TW0cBHGHkCmXsBeLhdW5tBmFRI5dH0ZSbx9l4Hl8EfRZ76IhCJIbhjiPTdd4IKHx3sXh3durO+b22+r1eKP/uiPePzxx9m2bRuVSoUvfvGL7Nmzh0KhwGc/+1k++clP8rd/+7dUuxMoE4nLsT6JRIJKpfK2HsH18MSuh8ftwKWc3U4bddtItA/8MXThqAneTUa5SjgLvSlorHYHRFzqnr/8/UbT1A9B3Q64Teg0rp8ze/0Hoyuvm0d15DB6/inIbUXyVkWQQATN7oS1s+jaWSS7w6rXsQFoFUAcNJSFwllk379BF87BqWfR9McgMQytJahVYWgnTLwKLQdibWT6RTSQtA8JvisSA4IhE7uNBlpYhGgCsikkFgYnjDZbcPE1KBcg101AiGUgHkdCfVyvg0mrRXRpCcrL0KpbBdkftIpxNACtMrz0L2ja4rsk0QeRNDr9IlSWkcE7kEAIufvn0dVp9NT34Jm/R7cchvH7YOpZsxdsPQyTRyDRj3vmCZxtj6LPfwHNDr+pl0XiOejfuanfV0Tg/l9En/pvcOjDiOen9PC45XBdlz/+4z8mEAjw6U9/GoBYLMaBAwcAyOfzfPrTn+bhhx+mXC5vNK2Vy2VCodDG97FY7F1Zryd2PTxuB/wRE0y53ejsszBwb3fs7J3o0quQGjcLwxsQxweRnp/oqcXxgROxNbyVx0d70VYVKZ6F3ADaqKBnvoNsfQjxh5D8XrQ0BcULaHwICUaRaB+6chzt3Q2zr0E6jzRL6J2PQ/W/wqvfgUMfgnUgkUSnTyHZPlgFqVbQ9gTc/Uvw3N9BKGB+XRFIJi3ntlKBvTvBbdh+I0EkEETcMNoeh/K6TSmLZSDdA5EUWp1BrjNAQXyYNWJkiw2oaLVNME+esEEOmREoTUNrGtaW0PG95jfu24cunkArK8j4+xCfH8kOIw/8Cu7KFLz4BZh+FR74n2D+FcgMwsQLEA7DzHHY/dPIg78Kb2qcr0JpCT33vA0aER+SH4P+7RtVd3EceOjfmeC97/HNG/M8PDzePO+BoRKqyqc+9SmWl5f567/+awKB60xT7O5PVUmlUvT09HDy5EkeeughAE6ePHmV/eGd5NZojfXw8PihSMh8u4Ii/YfR2efRdg1xAkjvXVCdR5dfQ5vrN3qpmyKBKJI/COntSMgH8Th69gm7TwQZOAztGjr3wmVrRXY3rF+ASBIN5iydITMCfVvNjjF3BunZirQDEAmjqR6rqjZd8HWQ1TPQ7kAg0B3K0YZEwoqzrRZoBGnHoJMBEmi7g7plJJ+GfA8MbYV2DebPQWEO2j5Uw5vf/BnoBKChsF6FegMJxZCdh2BsvzX6haKgYrFmp15GJ47D+gQM3gntKnrq61d5Y53cCM7P/CGM3Qvf/SuoFpDaKuSGYHkKJIA7cwTxB6wq/GPfwkhuBGffT+Hc9W+QOz4MoSh64nu4R76Ke+K7aLVo+33o36LPfglt1t713xkPD493hs985jOcO3eOv/qrvyIcvnxV8JVXXuH8+fO4rsva2hp/9md/xr333rthXfj4xz/OX/7lX1IsFjl37hxf+MIXePzxx9+VNXuVXQ+P24FgCrSDtms4kTwM3ovOPW/jgoNxS2LotKB0AbdwBon0QHzoTUdFqXaswUrEPLrie1szesUfhvx+6DTQ0jfRlfNIbhyJ5tFQBujA7BEYusfWntmFliZg/hhsfdgmxt39y7D2H+DC62hmAFouxBMwdRId3YOcPQptF519GeJp88CKmPANBtlITph4GfU51kU/sBOiW5HVabS8BDEHieVRUSiVYH3V0gmuNxrX8aHxjFWCwwnEF0BVrGs/EIHHfhNe+CI065bNungRZs+jxQIcVKRvFyxPoBNP2yjhvr0b593Z+TDuwE546q9h212w9UE48gXo64fzL6Ij9/5EkWDi+KB3HOm1vGMtr6ITR9D6ug3dOPQz6Pf+G5obMm9wMg+JHITj74n8Zg+Pm4obXNmdmZnhH//xHwkGgzz88MMbP//TP/1THMfh85//PKurq8TjcR588EE+//nPb2zze7/3e3zmM5/hscceIxwO81u/9VvvShIDeGLXw+O2QPxhS1doldFOw3ytgw+gc89Bbi8STpsvNrPD0gFqy+jSK9ZYFum5ToNaG+00r34ecbp2BTVB6nbefOTYJaF8qakt0mOxZldu4wuh/QfQySOW1ODzW7Pa2X9GExGoLCGxHhPywRQaqyPFGcjsQlaP4+77aWj9Mxz/Adz/S8jiOTTbZ1XbUBQp19C0QH4MZk7Yz9sdq+iKmI833mOd8xKE1QWYPWnRWv07obCA+itI/wAaC8PiMsTyFit2DWrnvLJq1odWA3Xb9rPcIOTG4OjXkAd+FS0twvNfguEdli5RWoBnv4veCxLPQKuDrs9DYRpGDiOxHABOohc3kobCPBKIoakMLE5YpNncq5Ae2+SFwF5Lf+hNiVKJZ5F9FvCv6yvo5MtoJm8pILMnYKLaTRxo2jHedIJX2fBeB0IQTUE8Y+f/eh9mghH7UODxzpDsQTJDP3o7j5+YoaEhTp06dd37P/rRj173vmAwyOc+9zk+97nPvRNL+6F4YtfD43bBHzbxWF1EQ2kkmIDB+9G5FyFt1VHois1oDxLtQTsNqK9a7JUTAr/PEhXEvorzw4dFvLXIsY4NVeg0oVNHV163509uNfF6ad/JUTR1Eb34LDL+MOILotndUJ5EF4/BlketgS45ijbW0OWzSHYrRHtwAlXcZM4qp+e+j6ZGLZptfQW27IdjT4GTgdKciVxH7GBUrUnN7UBtElyg6YeaHxp1sxosXQRRCPWghVUkmUNHw1CtgpY3P+h2x/4aR0IQ9Ns59gdhfQ1WZmDrnehzX7CIpV/43+Bf/zNk8jbRrVqGp/4Fvf8jSMQP2TFYOWci1h+2xAafH3a9H47/iyVF5PdA8RnoHUGXp2ywyLWvhGUktxtv+MDSHciRHoVo7oe//okcsu8Db/ZX4KZAVdFayeLnivM2bc/dxPusCo2KjbOmO7Uw3QvpPhPJt8igjRtK6EcPRrl1uLET1G5WPLHr4XG74AtDYx16DkJt0Sq8kTwM3AOLr6DVRcjtvurNdyPZ4F1ExNetKEaAlDWbdZpQmsAtVJDYIET7TGT1HkDPP4VWlpFY3qarrZ226V3zr8LAnbbP3F60XkInn8PZ8iC6ehIO/DTUvggzE5AaRlqCJvM2qjeZR9ZW0FTchL1PLSfXcSASgVIR2fMRWD4HaxdsWEPIh7pNWF4BHLMtjB1ACysQcpGBO8z7+0bUtclpzXUQE0vqOIiCFkOwuAgXj0EoBNU4fOMv4PAvWKNZ/Tgk09YQ9/SX0f2PIX1q9ot61c7b2e9AfjvSvwc9+wMozCCj96CpJBQXobSKuzz1Y744DviDNhwjcMJi1sIJnGgGMmNIOPW2/A7cDIgIEk1ZZXdw549+QBdt1WF1Fl2ehtmz6KWPEqEokhuxFI9Y2rN4eHi8jXhi18PjNkFCKbR0EbRjArJRQMszEBtE+u5Ea6vo7HMQ64PU1vfUm634gpDZ1b3cP4suvWSe4mgfmhtBLzwLez+CiKCjj8LFJyA+gDbWkVDCKpe9d6ATT+IWZ5DMLmTxJXRgrwX4n3sB3XoYCSZBAjC0A048C24U0nmzCwgmduMJKBbQI1+DYNgqdMkexG0j6wvggLoOrKzB609B3xaQGLpw2h5/zcEJ+Pzd8+0zK0SnjTabVuHdvhNW5mBxzby64Qg89/8g+z+E9o7DkS9DKg3lMhz7LjrdD4c/jPgc8IegsIyWZpB2A3q2weIxszmEMlCaQj74+zjhHy8tQdtNaJShVkBXpswnXLuAqx3wOxBLIrEMJPogvwOJZN5Tv0fvBSQQhr5xpG/8qp9rvQIr0+j5o1ApeCMl3gQytAsZ3X+jl/HuIGIfOt+pfd+ieGLXw+N2IZiCSI81aWV3I6E06gvB+qRFdkWyyNADaHkOnXkGUluQxOCNXvVViIiJ3PgQ7tLLEIhb9Fh1FeaOweABnHAaN5qzitncURh7n1XhQkl07CE4/Q103y8ivXeaTWMxDsVVWJuC/P3gq1ikVjxjArInAR3XrAydjkWROQ5k+mB9GZbOw8JZy+QNxaxK3FiGgR605YP5CRPKfVtMfF6Dgs+FQNjexMRBgmlIRtFWBQqTkMia/WDuIjTbJqhf/hrseBg+9Hvwzf8EsZhVjqtFePpL6JYDSDoD2XFYn0eXFmDrB2B9HtYmYfQhaBbQJ/4POrktm5/vUAxiOatgRpJ2i6aRWBbJj8OuR+0IVNHSAky+ihYWYHkepl5Dg12bixPserC7NphLN5//nXvjfqcQp2szCYATeHttCD6Q3n6g/+3b5+1A15vu4XE9PLHr4XG7EIiB20L67oaVY2gogyRG0PgQlGfQYBJCaSQ+ALF+KF7AnXkGSQxDOAOB2HuqSie5A+jiEaT3biQ9is6dtsv1wQgMPQSnvwKZcVg+DT27AHAiWdyxB+DUf4e9j+P03YW7vgb1EqzMoqlJJJeDUAIZ3oGefOZyugTdZqqWjfWV6hIaCkMwD4Eo1ApQWYGZJTSchFwfIkUY246WqjA3cf3KyaVpdI4PAiE0GrfRw506xPPgi5nVYXAcyquwMAuBBJz+HhQXkMc/jX79P0Cw3Y1Ga8PkCdQ5CKUT4AfxtZFWGQ0loVVAihdQXwjJpSG0uWDTdhHWVtAV1wR/p203XxBCCfBHkGgKSfYiqT7Y/0EccdBWA+ZPoyuTgKKxjDVzXVmVctiIw7upUNea6xpVaDd+rCmCHu8w6iCJ2+QDwnsgZ/dmxBO7Hh63CSJiXf5uB8kfREsX0eVjkNsLiVEbOlGeRh0fhPNIeiukxqCyAOvTaLO8IUtEHBPP2gG3ZbFlmz6nY01x/rBVNX1XfHX8P5F4FscH2b3oyjEkfwCqS+iFHyA7P4Dj+HD7DsDaBTQQh3Z9Y0qck96CW5xBp76PM/oIzpaHcRenoFKCqWMw9stIfcliw8Rv4i4QMnETwJrTwlF0bQV8DkSK4A9aXFgiC20XGkWYm0DzY+BWkGwScnnb3zWoRY+FEoBAuQSrszaGuNUADUJ5CaIppNVGwyEY3G7JDcEELJ1Dv/EXyM99Ev3B/w3zpyAWhvUyTLwCw3tgcD+68BLMHkV6dqCOwtp5SI+jnRUktnmDj/gj3dcqAK5Au4U0ymizDI6ikTjqtmB9FopzUK+YDSMYQXq3IQc/DCJIYR7q69Buou2GHVe7cTmR4abiyvW+8ff31hUL72XEGxng8SPwxK6Hx22EZHehK8fMxpAcQxtFdOEIkj9g6QzBhImX2oo1WwVTVuWN9SHqdiPIXBPNrXI3hcmqnqIduKZvH3DVRHGnAc1yN2XBRgdftbV0Z76LmJC9JJLjAya6NjueYNyi0UoXkfQ2tP46ujaJZEaR9A60cMEaqmaPIKMPXX7c6IPo8X/CXT6Ok9+L7H4ILS1DpYSeeg4Z3oIEYhCKoK0WpHIwW7LBEoEAxOLIng+aD3fxIhTWTBz7rTJLJAPStGpsW9CwiySjlrBwDWqXw/1+O2EhP+R7oafXmpmmzwECvgTaLNjz+zvQPw5LFyzOzG2hX/ks8jP/KzozDie/BwmFeh1mTsLyJCSi4Peh6X5YmjC/caMMDRelusmyFHHWzbIhLuBaxLDjQ/wKgSTSaNo5iSYgFoFYyF7HQAKtrsGrxy2hwNe1LaDdXwq94vubTCD6Al0rRxqiafsQ4tt8gpSHx9uPl8bwVvDErofHbYT4QpA/aHFeyS02Wa3nTnTlNQilITlmzVyxfqvQNUtQmblCiHbFqHSbn+QK/6VsUqnt5vFu3K57yVcvb6udbvxY2y7jz7+Iis/sFInhazySEh9EV47b+uMpdO4YpIa6ww4OovMvQzSHFqaQ9Ig9RgS2fwA9/13UH0Ty+9DBY3DqKMwcR/c/ilTm0XjCBkKku38q1TWxow0Y2o9v/AG7jF2cRxdOo0sTMH0cGjMQTliSQyQEHdD1KjiNzY+907RKp7gmZgNhJJKwZqaRfVZJnTlrnuB0jw3roIL2jcLCtK0pGDErw6GP4dz987hnX4C1i0ALqmsWI5bNIdMvIsEIGhpFls7CPb+BE968sqvtBtTWoVaCegmtrSONMnQ6aG0F0Trq90O9hZRbNhY6mrEKeFAhlwUEQsnuTORuhNulN9Wb8bJppw31MlqYhvkTUC9vxIp53BikfxfO1ntu9DI83sN4YtfD4zZDHB/kD6CrJ8DtQSI9SO9daH0VXepms6a2WQJCKGW3t/pc4rOmJN9mjVk/5HFXfK+dJlpbQmtLUJqySLLMNhPql8juQRePQGobtJow9SKM3YfE+tFoFpo1tH0REv0bVTgJxSE7jpZXEV8Q2fdRdGHCbATPfxV2HYJQEAquVaQDAbvk7vNBwI8+/V/oiB9CMaRnDAZ34Gx/AJYmcI99ExbPg1OGZhqSKSSkZnu4BgUiqBu0tTcalnZQWDUPbzKJBP3o0F7zFi9egGAUHRxF2utoLg8rq0DbBPZL/x23f5tVqyeTZonozEBpBVZWQBTd9mGYPQ4BPxz5Em43Y/nNvELiJNF2GEpFkFnUca2KW0tCJYZ0XLO6hJLgd2+t4fThWPfDjDVwSvdD4K1cGXtPE7l9Iu82ig7v1L5vUTyx6+FxGyIikN0DxfOWtxsfRsJZJJy1BIC1U5ZmkNpml/Nv5Fp9QSQ+hMYGoL6KNoqwdgZ1gji9By8fT/6AeZCjOauw1gpIJI1ktqMrp8EJWLTa0ININwJM+vaiZ76NBgJIOI/u+yl44Z+gMIuGfhoJzQFiVoZIFKqV7jS1NjIyCijqttDFE3DuOcufzQzAvocskmz6mPltqwWzDwQ2E/1izWjxPMT9CN0GKHVt9PDKPDq4B5wy4ouiDFm1+fwJdGyHZQDnM7BSgnoRegZg/ixaWoI9j9l6s8Nw7ilYmkXzPcjqWbMwJIegWYN4eJN1vQFfyCwTgSgkBnC6AsOi0lrQqqPFeXTyeat0o2aV0DrSWOGNFpebmks+Y+3aMa78mce7T2YLjHmJDB7XxxO7Hh63KSIC6W3o+jRaOAepcYvoCsRMOHaaUDyP22lwZcXKms6C1rR0vcvQjr878rd7c4I/cZKDiAORPIRzEC2ji6+ilUUk1mv3+0KQ2mLDMZIZdOIHsOdnTcAHI1ArQ7zXbBEDhzfsEDL2ADr1PConkaF70DNPQ3EFnv1/0b2HkUC34hqOQ6XrU261zHbgCyGRLGS3QLQPcNBz34OX/xWyI7DjEDQqcOYVWJ3uXsp/44HRrdQoiGOCWRwTxj1jyMgBdOk8NBvo2AEkH4D582jJBC/9QzZWuNeBxQLMXIStu60K/Mo/w/A+aNftiWrrUCqBTEDvnRaltnYCWvUf/jo6AXt8vQyVZZh9xX4vwimI9SCJPhPA/TttXDKgnTZ67ll04XWUJrdc1fOq32e56ovHu4v4Nvf035KIvD0gBfQAACAASURBVHOpODejrejHxBO7Hh63OZIYRmvL6PJrNlAi0mui1xe0RrY3bL8xztdtXb+a5Xb9ts1ytxmtifsWCl/iBCA5ggSuGBMsYo10+b2WMBB5H+LYnzIJ56z6mxiB8gq6cBLp34OkxlFnCtbn0Fg/svgKmt+L+EJIMArBOOpPIqXz6P2/Dt/8j1atXF5EgwEbBRx1rBmr1TJ7QyeEIjYmtrSAOOfMazuwA3Z9ED3zTZg5D4E47LnX/M+beZYVaNSgXrPncd3uee1AeRZtrlq+bS4DU6+joShk+5BYDg2GYHEa1guwZS/S70OXgbOvwbZ7IBeC9Yvg+m3QA/MwfRaSd4J0YG0K2f0RS5jYbGGtOrSq0KxAq2rLCsTNO+z4bZ3VolWgl06jgai9GUfSkBpBdjyEs/Nhq/7eMqgd96WqrnZfLy+C7Mbh26zx81bFa1B7K3hi18PDw8YGh3NQXTDRG+2DaO+mFYSNcb68uWrKm/0zqqrgXqouN5H0DiQQ3bjfCWdwU6Po/IvI4P2XH5jaDotHYfAQnH0SzY8jwYSJ9NxOpDQDOFA4hyaGbWra0CE4+x00N4zjD+AObLNhEAvnoSdrFVltW+Wj0YBIBK2vQH4QaYM262jbhfUlWJtDcnM4ez+GVtfQi09bE1mjZY1kmxEMQ6rPOvzDCavs1oro+RdgbRoyeXCakOsBAjB9Fh3cgozsR31+WJqBUy+hw1shPwjJJJx7ATL9Vs1mxRIcAJp1tNFGlk4ggR5Quf6Y3ze4G97opaa8CL4A2q5ZE1t5HvxRy9mtrlilGkGCseskUXh4vA1E85B8d8eae9xceGLXw8MD6FZMY/0mdDdEb6/9+1J0WKdxOTqs0wRt/8iqnTj+7vSsYDdndhPZK9il8kuTtsRn6/GFzFvcaUHhDK7bRjI7NqLIJDWOVpdxS1M4ySuSFvL7LXGibxd69jvI7p9FsrvR5ddsqIITgOI0EoiZUIv0Itlx1O2ghTPI3b+C/vOf2fMqiPjRdhsiEavABgMm+E++1BXRAzC8FwlH0KWz6MIkWvkyMnYfcvB/gIVjUDh73cuE2qjD+iq6NgkuVu0NxyCZhf5tMHkcVhZtalffKAwMwNoCmlJk9ADqhCxObG4OogFID8DOfTBxGppVSPRAqG7VXV2Ec6/BnjvQ3kF47V8gO7r5ixfqNmKFEzZow39ZrIsvCKlhmxh36ThaNSjNoOtzNmSjWYVAFG3WrdnOw+OdwAkit4vY9cYFvyU8sevh4XEVV4ne2iK6euJy7u2lWzCxIV6dH/IHUlWtItppbojj62wIrQq4Beg00asyeBWJ9Jilwm1D4YzFfWV2mk+35yA68wwa7UW643jFF4L4MNosQaeGuzaJkxmF5Baor6CVRejbj84eRQYPQXUeyW9DT38LRu9GiufQ7ffD8e9BtW4TzZpNiKetsosgIRd27bfJc4UVeP1pGz+cH4SxO2DxFHriW8jAJLLl/cjAwR957rVdNxtGbcXG7s6eg1oVeofsnF98DWbOmejtHYVWCS20kaFxVPywdA40BgsXIZOBve+H09+H4qw9QToPRReaLbSpyMoJZP+HN7cxqJpYrZegtAD1ddxO9/VzO0iyD/p3IVfElkkgArntSG5793gaJn7L82bP8PB4J9h0DLeHx2U8sevh4bEpIgLRPiTa95PtQwJWSX0TqQ5XXS5XF2pLFpUGSKQHDSZg5TjSewgJRNHcTnTueWTkkcv7iPZCbRnd+hCc+DruoV/FCWfR2rKNEV58FYbvRWdeQPr3o80SMnSnCcZEGtlyD3ry+5bAEA1Dq41EHVTEGrtcgdIionO24MEc6o9AYR1efgIGd0A6bfm7xVlk/H3XHY6xkVXsC0A4i0T7kPx+GH/UmufOvgj4YGwvlNetijtzFnq2QKiBShN6++1Nf/Wine9yE9afhS0H4MLrlvAgHYjnrOp67jXYc6B7rtKbryuWAYaueV1UFdYX4eJR3GbFPmT07YDM0FU5yOIPQXYcyY7/2K+9h4fHD8Pz7L4VPLHr4eHxnkbE2RDdJnyXoXjepoY1CkgojcSH0coi7uoZnOyOyw/O7oaFIzB8GM58A3b9HKR3wNJR6DsAS8eQ/jth9Syk+iE2gLaqENkDxVPm1XWboB0Ex3yqAMUS5Mdsuhh+iKShVkSqRQi00O3jsLoGs6vQvxU6FfTk/2fV12sOELss6fiueKtRuyOahcFDyE89AOe+bx7eRBK23wsXXoHVSYsDizYtMSKVgcR+WJ6FwqxNNps4YR7epTlYXrZosvUlaCvaaMHZf0WjPdc//4EIBGMQ7DamBePgC1plN9mHgE16WzyLzryOopZh3DMOqYGNmDcPDw+PG4Undj08PG4aTPj2ItFe3MI5dOU4MvigVZB7DqLTT6HxQWuIurR9bh9aOAPtBu7SCZyePZDdi66dQRJDUF2CQARcByqzJoynXkBG70Hzg9ao1mx1t+mYMK2UYLUOw7uBKtQLEI5CchDqJWTtAiRjaE8WFhZhrWSTz/ybNKhdsgu06qjbsefodJ8nWoLKChLNQP9+ZOvvoSe/C1NHYOshmDoOzXUozIP2QaEAmTwyPIKG/bAwCdkcNF0T5o4P3IalO7RqcOEs7D8MjbXrnnOtr1jSgOPbmKCHz4/6AkgoCdGcWSx6tyCDexFx0Po6LE+gM6+hqlbhvZUa1C75JsUBBBzBwv5v9MJuUxJ9SHb4Rq/i3UHknfPWep5dDw8Pj/cWkhpHK/O469M4iWHEF0B7D6Jzz8Ho+y/n6AaiEM6hw2k4/33cYAwnNQrRHotPa1XQ5CgsvIYM3om01yGUsEzZsQOwcAEqFXSgBxolCEUg4drwjZPfB3Ug3Yf4Bfwl1O9DenfBygWkVYXBUbS/CWtroK3NDyYcg2DOfNCBkE1p67Rg9gKcP4Mm01BdRSKvIb070fH/GX7wf0EsaWutLFi11heGhRm0sg7hHsiM2c/92H4bNVhuW7rDYskaDC+cs4a2zfAHzRrhCyKi3XHOTRPt8SxaWITli/ZBIJpGomnUbw2G5AZN/Dp+S2dwr+PXvtnYLHJsI4rM44YQjP7obTxuazyx6+HhcVMiIujA/TD1bdxoH44vgBPJ48b70YWXkP67L2+bGIbl19AdH4Qz38Ld+VM4sQF05XU0tx3mXoKhw+j8K0jPdrR3B1x4Funbhbbalr4gbWi7EA7bQIn+XigVoFyGlfPmY43lIJKA+kU0nkPaLVieQzJDMHKwa3t4A6rdtIuWWSbcNhYB4YORLej4XpiZgAvn0EgEKmtI/DTy4C/jTh8zwZ3eYqOUtQ2NdtdT3LZIJn/YqsW+bi6uP2zHEgpb01h+GzJ+F/j8V0fNdSvOWivaMIpOuztcImyV28VjNip5dD+EQlBZQosn7DiCMYj3INEE6g/d0hUjj/cALsBtIng3rii8Q/u+RfHEroeHx02L4/hw0ztg8UW0/34bhpHdjc4+i1uaxklecWkztw8WX4Ktj5j/dcf7IbMbll6Gnn3I2nmI90OtjAQaaHarVXf9AXszbdbNn+oIqCKRDISikHdNWPqCaLEEs2ctoaHVAX8ATQ4hhdlu5fU6l/IvNaddeXMc8DlIuwa9vbBlP7o4A+deQoMBdG0Rdn8AHvkNePlLEEhDqwwhgWYdSm2oVWDLfosgq1YgEoP6utkmsj2wsgDzZ9DFCXA7Vw/0FbHKbiAMwQgkckiqH0nmkewgOH50bQY99yyUFs0aEc8h/jhEUiaUi3M2vS0YM7Ht4fFOkNliWeEeHtfB++vj4eFxUyPJLWhlDl07jWR3mX2h75DFkUUyNv6Yrn+350508Sj07kMvPoeMPYBkdkJ5xqqrsTw6/xrSfwciRXRhCrL9sDIDtZpVKTttq4C0fTbAwh+C1jpUF5BYELZsRVeL0O6g9YaJPUmg0dj1LZ1ud/xwo2zfX4rp0o5l42aGoFlC4kG472fQQsV8u0/+PcR6oG83hKegHIXSMkRDUK/Y7eQLML4fTq2A32/7DsW6MXACqZD5bd9Q1bGhHi60Oxa7Vp5DF8+gzZrZIYIx6NuG7H4E0v3I4ll0/gzabsHcWasuh6I2zjmRNkF/q7Ax4pmrc0+9CvaNIZa70St4F/HSGN4Kt9BfHw8Pj9sREYH0NrQ8h5ZnkPgQ4o+g+T3o7PMw+uhl/67jM8G79BKE8+j8y8jAIQA0Mw4Lr8LQfej0M8jAnSY8R/fD/AWo1aA3a4JUfTD7CkwfMYHj+CHU9c+mtiK+aVhZQJO90GqijQKsraNyncEKTsBGDTthiKcgmoFoGhpFmDlqObnZMUgPQ3MNibTROz4K089BwAe+MkTGYPEFa5Yq18xO4atZLvC5VyAeh1IJ4kmoV63Brm8Yzp+B6SkbfSxX3BzH9uX32y0QtK+hKAwM278Li+j3/g46LhrPInsewRnZbz7W0gK6OoOuzcLazC2Us6sWloFe/b3n2b1hyMAu2NN/o5fh8R7GE7seHh43PRLtQ9en0eY6XIoji/aj8bVr/bu+AOQP2oS4oosunYCevVA6D9kdSOEc5HZAccZyZp2IVe46HQj6kUob9QXAn0L7tkEkjRQmbTxufRVKM5AYhJG7kMWTUF1Hdz5ignUzQaQKjQpSr9kQhkYZlidsCEcwAEMHkGgPnP8urE5AbjskB5DKRXTkbjh/FGIFJAR88LfRtTk4/zTMTlkcmdu2gk113Z7L8UGrDdEkNKrwgX8PrRa0GrZ9u2G3VgPaLXtMu2Hb1utWxZ6dAVzLH06krMGu3UJf+if0xa8ih38eZ/QAkh58l34DPDxuE7w0hreEJ3Y9PDxuCSS1Fa2toqWLkIshTgAyO9GFI7jFCzipLZe39YchuweVk7AyD85JSG9BOi20uY6kRqE4DYleZHkCDQYtfqxZBwTiETQ3aIJ48lW03bRqZigGsTxSW4LJRdhyH6xPIye+A31j5n29duUQyYPjIJfGMgPacS3B4fXvo+kUMnaXTbY79Q1YOQvDh5HKLDqyE5ZW0PIyMvF1JD4Ehx6H9PNovQRL03aM1Qrk+6C4ZlXedhOKNVg5g/SMQigABIDYxrLwh66NS3NdqBaguIqWy7A4BatFQCGVBL+D/uDv6TzjwOhupG8r+MOWt+vrDhjZ+Bq8pZtiPN4lgnEknLrRq/B4D+OJXQ8Pj1sCCWdN6GZ2oasnIbcfcfyQ24vOH0HDOSSUuLx9MA7p7VZsXZ0Hfwh1/NCzH50/CkMPwNQPbIxxPAPLc7BeQn1+m6ZWnUPFhVQYfGmI5S2GbOY0Wi1BOotMPA2pYdj9QZg9BtXatQtXrIrbbnX/ASAIal7X7dvQwhr63NdgYBQZ2g2+GEw8CakhRNchE0b1TnTyaXBdpLECkQASzqNO2ETr5Akba+zr2hLKZbM6TJ6GanOTE4pVgYMRi3a6osFMIil0fDcODXR9HoqLaKVkDW+NNuRHoFGBc6+iE69D3xAaSdjgDHEu2w5v2kqSdO2N3fVvHMfNejw3OekxZPzhG72Kd4crPeLvxL5vUTyx6+Hhccsg6W1QnoJoP6xPQnIMCSbQzHZ04QgMP2K+3Uvbh9KQHENdF9YWoG8cSueR1BgUL0AgatXRkTtsKlmtDpkMtFvIwf8RRwTX7UBhAlbPQW0RkmF0aDtcPIk21sGZR9bnYcv9lvl77aqtIhxOof5Qt7rbtGrxme/CWgkZuQPN9sHMBPrC12FwDEbuRWZfgQqQGUaYQQ8+Di/9EyoLMH4I0ToSd1FSsBqH9XWIJaxiHI2Bo+A4aGnh2mWpdgdcdKPMuksFUBSCYTQURQJhe/7hu2GrD2ZOooUZiPQh+96HVtdg5rilUah2La6XRP3N6nPtil3xWSbyRoLGrSsW3tM41xnD7eHRxRO7Hh4etwwSTKLr09Yw1qmbJSGYQOJDaLOEzh9FBu+5+jHRHtu23YLlC5AdgWAUXZ6G7E6oFSGRtapHvQHxOLKyjPvkf7RGNQnaZdRQHGLDaN8AMvsMOjgIEoWpk6g24eKz108kcLu5uG4HFceqqE4Acttg6C449W1kcBzu/Di6ehZOPgPlAtozAq0WsnLBRvMuvwgHH0VLNXj9W2i2H9n7ISRwAk1aogTFAgSDZlsoFGHfvUjP1k0WpebbbVY3btrpDoYIhKC0BOsraKVsE+XmTtpgD78129Fw0ZefhmgK59AvQ2YAfIGrs3xvQrTrYXZr3cl1a7NQXITK2i3UhHeTEbudxK6XxvBW8MSuh4fHrUV2D7p4xHJ1105B7oBVczO7zL+7fBwnv/eqh0hiBDoNdKUGhRm0XYX+O2HxNRNo6zNoIGgNXPU66rpIbsiqev4AqIs2q9bEdfYMGu4DJ4xUZ9Add0C5BPMXrl/HlO4bmDgQiUA4YcMrVs8icy/Z0IiVFcCP+ILw0K+jr/8LzJ6DXC/qSyLlZbMr6AVE2ujB98HkGfTpf4Ddj0JvP6wXTPCqWnU3FIaVCbR//JroMcDsDpHY5WWCFWMLc1bJ3P2oVaMXTqNzZ9Bq0bzA0STE0ubhLa/gfuc/g9tNL7jmmG/CN1hxLmcj+0NWmY+nES9L+MYQv52ixzzeCt7/TA8Pj1sKEemmLbwKmd3o2ikktxdxfMjAvbhTT+IWziKprcgVUWCS3o6267B0HpohpDxtVbxIFqrLNjShvWKV0WgEKnUTap0WdBqIdodLhCOorwRLS2huFJamkEQS9j1gl703o1WHRgVtlC01YX0JFivQqKOje5BOFRqrMDkD/XcgkRKM3oHmC3D6ecgNoe0OBPxIeRVCKaQxB9kYmtsDZ38A6TykslCbgXAcKkXL2F28AEv/xY712pN5+XvHMQ+vzwfBEOQGYH7d4swSOeSuDyPxPitunn0Gpl+DWskGU/TtQmLprjgMoI4PuCSubzIrg2Lnpd0yX3KzYoM6ivOoepXdG8KmjZ+3KF4aw1vCE7seHh63HOILQnoHlC5AOINW5pDYgN039BA68wPLvI32bgydAJDcPrRdg8ULNiktnIdA3DyrQ3tsNG+jCQOjaKMGnZaJ60jUqrFOCIrzyPIs5AfR2iI0Ba21ker57rjdzRbsWBqDBCHaC4kAhJNovQwXXjQ52DeMBMOw8DI63YKDH0c6TfSuj8CJJyAQM8Hs+pCIgBODQASpLEBPHp2dhr5Bq+5Wy5DqgbVlGBwFZ5PJbqqXvbpK17vbsq+1Glw8ZZft/UG0MA+VOYtki6YhPYQceAhCabRWh9lTVvV1XbNsaKdr3ejcfPm06tp5ABP/4lyeNncLi4X3MhJJ/OiNPG5rPLHr4eFxSyKhFNosmQ+2VbBqpz9qCQ09B9DiRQhE0Na6NVOJmHDtuwvtNGB5GnoCSLOMhhMgfhNmzSY4goweBF8I2i6sLaGVBUTXzA+b2AHL5xHHh+aHkXIVbQtcp7Ar4kA0YRXXcLSbvbuOVObQ/IglJ0xfRFNJq5CGEvDqlyGSRcYfgO33oSvnYX4G+kbQtRnIbkFaVUiN2ZS1yj9DuQKJjA2aCAVhHUto6BsxH67Pd3mgBD5E9LKAc3zg85uneH0F1kvdOLaaTVlbXDUR6ExDfh5NpJFgBAknoC8Dkn23Xvp3HJsu17bqbvuKDwEeN4brTOG+NXG4fFXkndj3rYkndj08PG5ZJDGCrrwO0X507TTkDyLiIOEMVBah40I4BeUpND7SFbw+6L/XBjyUptDYEOR3IjNHbcqY60KxgIyPQHMd1QqkY0h8AFCoVNHSHJLuAxdkYQJCcaR39Kr4ritRsCpqpwilVat8qoA/YqI1dBENh2BhCq22Id1CcoMmsI5/HfLbkMwWNNELZ182y8LKRTQcBp8faVWQcBxdW4Z0FsqFbvrEFliahItnup7a7mq61VYVAOdy38ql2KNAwLJ6wyGbqFarmxXD6R7M7Bz4ltBIDII+iHajy8S5XAkVuXrfNwviID7f5WPZ+Pl1Psl4vPOEb6fK7jvpc7/Z/jP++Hhi18PD49YmuwcWj0JqHF09Dtl9VsHN7kRnn0Mid0Ck52rB6wvC4APoma9aBS+aNTEXjNjQh/USWrG4LnECkBq3wRC+EFqcQMKvQ3EZbbtIqh9tVpGpE1zvzUQQrvauKoiDxhNIGQj4Idtnntv5KZi8gA5sQ8Jt6BmCTg0mjiKD+2HPg+jCRaivQShuUWC5HsQfNa/x+rrl63Y6dkl+9M7NUwTUtaptq2ZNZ+3uNDbXtUruWuHyqFynG8XValukWchvQrwwb5f3a/HLx6VXNKrdbBYGsPg07R6HvrHhzuOGUFiHkftu9Co83sN4YtfDw+OWRsSB/AEbD5zcAoUzkNlpgrf/LnTuRWToAROr5Wk0PmyCNxBF40NQW0KKk6g/DOkeWJ2BdgedPwnRjAnKegnWzneHMMSQrT+LVheQi09Bo9ZNNYiz+WVCZSNO6FLlVBzTu7UKWl+EeAo6RSSaQIdGoKcHzp60hqgBQQICO++Dydeg00G2PYImJ+HiSegbgOkJtDeP+MNooGy+2moJiguw9z6zGmy6rkvn0AeBqOUO+yNQLaMTL8PqtMWTOY4dY6MM5TWo1iGdsw8HzYZVkjcVtnITFpPEXmfHD4GgVbbDcbs5XnX3htC77Uav4N3Da1B7S7yrYrdQKPCpT32Kp59+mkwmwx/+4R/ysY997Jrt/uZv/oavfOUrzMzMkMlk+JVf+RU+8YlPvJtL9fDwuIUQXwjSO2zCWrQHLV1EkmNWwc3ugOVjSM8BNAxUptGYCV4GDsOZr6KtCgwctErn8pRd33fjMD+DtqqXc3LVtWELI3M4AweRfb+Mu3YBufAEGk1tHk2lVsXFF0AlCMhGA5dUHSgXkMVJNJ6BzBAsTkAgjOy9A63U4eQRtH8Uab8E/bvNW3zyX5HUALr/ATh9FHKjUF1EIynEH0AbDau4Og4cewEGdm1y0oBoBoln0XDcItYcB2lVwC3C8JDdXNC1NVi8aMI3NWBvmsszVpFOpCDfa9Vf5w2X/t3OTRfGYNXpDnTUqt2dpg0TKc/fhMdyi1Begp23yQQ1j7fEuyp2P/vZzxIIBHj66ac5ceIEv/3bv83u3bvZsWPHVdupKn/+53/Orl27mJyc5Dd/8zcZGBjgIx/5yLu5XA8Pj1sICaUs6aBZAl8QrcwjsX4k2oPWC2hpEkmO2nSwygwaG8IJRPn/27vz6LrrMvHj78/37ktys29d0qYtKV0pLWBrRZYpoCyC1KEchZ8wlIFxmB96epQRRxAUnBkZOVT0J6OjjIh6XGgBURDZVBapXaCFQre0adPse3L37+f3x+cmbWjSJm3vTe/N8zrnkuTyvfc+nxSaJ5/7fJ7Htpxm57Z8Eex7w9S2JiI4zvzEEa+hExHsHS/Bzk3Ye9+BGWdgTVkGZ/wfaK8zCfFwEhGIdqGiXaZMYoAniAoUoWPdqLZm9Huvmhrj4mp02x7wh1BnLkfX70Hvbwdbo1xumDoXOppQOzdA9Tx0Tyt0J8HdDw43eGJQWg7794DVg27bNUJccXRq2IW52akWZRaEKmDeClT5ZKz8Vigtw07GoaMZmvZCYanZ1e7sgGRxqh9x3Hz/BnanBroZZBvlTMXvB6XBssEz3kFNYBXD/LKWs1K17ml77tyUsWS3v7+f5557jqeeeopAIMCSJUu44IILWL9+PWvWrBly7erVqwc/r6mp4cILL2Tjxo2S7AohTogKVJjWYsoJ0S60w4PyFqKKZqEb/4Z256O8BamEtwEdqILS+dCwAdWz33QjcJj2YcmX/9+Qt/2U0w3efNSUM1AzP4q944+w/Q3sXRthzgVYlWeOaXqYtpPovoPQuh0VVVA2FZWIodsbYcerZkSvw4XubIaKClRVNXrnVrQ3gOp9zxxEyw/B3o0oXzHa8pjyC8syFcKO1FvxlgYVHj4IjwUOtxnKYVmD69Uut+lCsekX6EgYbVtm8tzsc7Eqq9Dlc833t/sgNOyBpjqTLA9ufarUlLjU82YTZZnEPdUzGI/fdOXQOqffBj6leb3jHYE4xWUs2a2rq8OyLKZPPzSWcvbs2bz55ptHfZzWmg0bNnDNNdekO0QhxASgQtPRbdvQvnLo3W+SOVcAyhehD7wGlWehXEGzg9nfhCqqRTdtRPc1Q1ENeN4Bp4K81Phb86zoZMzUrL71O7TDgZp/KWraMuzdr8CWZ7G3/H7kE/tuD/iDpv2YP2CSS2WBvwSmnouynGZMcNPbqFARFJaim+vRHQdhUi0q2me6QsyoRcWSpjy2vw+ViMCM86D+r2AlIZyAgEIrJ4o4elI17H7fdFP4ID34D/PPw2sFLQVeH3h8qKJidCAI0Qhs+RV20gYs8BaahFA5oPywccRam8Q3HjFlDwO9fLOFtiHSn2o7FjclDaOlDivlyLYk/1QWj8Ps88Y7isyQmt3jktGd3by8oYcg8vLy6OvrO+rj1q5di23bXH311ekMTwgxkRTNgZZNEJqJ7twBRaebut6KxeiDG2DSh1DuPFOrm4yCOwjd+2H6CggEIRZBde1KDRPwmOENTi/4/ehQOXQ3ozevQ7sDqNP/DjX5THT77mETHK1t83Z/Xyd0dUPTQbRtm6TKaUHpO2b0btlC1OmfNEn13ldQxXGIhtFNO9DefJTLhbbCqbHFYXD40fllqM49UDIdtW8T2ldgkt5oBBzKHHybdxbDNwDW5nBZLJz6GDEdFrRtkrxIBML95pcChxPyClDBIrAcaIdKlSxEUq3K8sDpTe0kmxZkKtuTPcthSkJcHvPR6eJYby/raNgc4OvtNBPs7MMSZZfX9EAOFkAgJIfdxiJQMt4RiFNcxpJdv99Pb2/vkPt6e3sJBAIjPAIee+wx1q1bx+OPP47bPaG6Rgsh0kgpBaUL0U0boXgOuv1dKJ5nEtbi2dC8xdTo+svM7m/5mbDneVQygg4WQHMD6iO3ouMx6NoH3Q0Q64FEUzBlqAAAIABJREFUGNXbjrY1lNdC50H0279FewuwJs01idEHYwHwAiE1ENzAvdixPtj1OvpgM7Q3mUTbXwKTzjKJ0u7nUR4vuqcdrdyocATiYbTTBZFOCLehfcXQvRuVnw92FLrCEPShbRsV6TGH2jwFI3+zLKfpxOD0m4TM4Qenz9QMv/+K6egQ7YXeLnS4x8SvMYfT3H7zy0CsI3W/Nv9Oa3Q273AqCwbaxWl7aDu10frgML1EFzQ3QX3EDC7JxrZs46VyJnx09bGvywmH/n5Iz3Pnpowlu9OmTSOZTFJXV8e0adMA2L59OzNnzhz2+l/96lc88sgj/PSnP6WioiJTYQohJgilHFC6AN3ylkl427ZByXyUrwgd7UZ37EIVzkA7/SjLjXZ60W3vofLK0Q316HeeAn8I8oqhsBLUJJTTj46GUU3boP8A2mFD3iToaMSu/9sIQyWU2RV0eQ4lhABo6GkHTwAmzYe+dvSBPRDogp6DqEAp1H7C1PTyFoS70U6nGUKhHdBxwCSfbfUQCKBtUPE4uqAEdAyi3ZipF26Ua+SaR62TJpGPtJkDdskYJCOmN/DMBVA813Sn2P0GuuEdM02svxt6u0D3mqlslnWo5newFMIaXH72OeyAnfOw2uMTWYsF+NzmJsYm4BvvCMQpLqM7uytWrOChhx7i61//Ou+++y5//OMf+fnPf37EtU8++STf/va3+d///V+mTJmSqRCFEBOMcnig6HR05w5UaAa67R0onosqmIZu3oLubwVfMfTug7wK01Gh5Axwv4Xu6gflg+hBlLbB6UHnl5jpYhWnQSSC6toPkU60z2V68qrh/srVEO6BrpZUK66BEgYXVJ5mhlI070RFu9HFU1EuL7p1H7p3Lyr8GyisgTl/D7v/gOptRHcM9AQuQlXNhN4OdP17EGsxB+gsje7shaAf3d+Panwb7TvK28DKAa6AGVHsK4T8YgiWm2Svcyfsf9kkwT4/avHHwFeMslymM0PnfmjebXoTx8KmRjceM2UQ8USqLCLLdjC1bQZrDPxZDezo5nC94ymvYJia81wlNbvHJaOtx+666y6+/OUvs2zZMgoKCrj77ruZNWsWGzZsYPXq1WzatAmABx98kM7OTlauXDn42Msvv5x77rknk+EKISYA5Q6Cv9wMjwhWQef7UFhrdn0bXsfyl6DdqbZlrbshWIYqr4baFVC/Fd26H52MmwEDvW2oZBgmL0L5A2h3NfR2o6K9JsFjmEllYAZOBAvMgS6HE205TQ/X/dvQ778GlTPRVdMg3ImOxc1uciyKbmiBRBzVXQ8Vi6BqCar+LxDtRve3ofs6wZ+Pmjkf/c4baCsCfUBhaiKcUuhYHJVsPsZ3SQ/dcbZTN4c7lQBXmtHGHXXQ/j4aG6W1WU9RIZRV5uwPUq11qoa5N5X8iowrmDreEYhTXEaT3YKCAr773e8ecf+SJUsGE12AF154IZNhCSEmOBWoRLe9a3qlukPo7jpU/jTwFaPD7eAtRDna0L4QNG0EncQqqIKCKiCV8HS3oPe/YzokbH8VnZePmr0C5fSho77U2/rH2MW0E2AnDr0bPnkOOlgKdX+Dt1+B0hqYNB3VuQ+dXwZOB7S2o60OM3HN6YaqReDwoBo2Qbg9lfh2Q6gQwn0QUyjfFDNgIxCAzn6061g/Cg5LVLXpM4wj1Wor1g6t7SgbswtsuVL9c53g8oE3aup3lXPo2/zZtqM7giEpfI4m9OJUIjW7x0PGBQshBEDRbHTz31Al86GvCd3bAAU10LzF1PEGKiA0BRrfBhzYO16EompUfiXK5YNQGSpUBoBu3Yf97ivol36InjIbNe8yVLBx5J0/NdwXGh3pQnXUQTAIFR9Gtx+Et15E55dDaQiCTqg4C/a+DXvq0KVFKN5mcOhDsAR6mlGxXrQdhe7U2N7W/WaqWU8HKt9ndmDHQOMxr4FlOjVEetGJmGkj5rBMCzWfH5VIQldPqr1YbiS3R0q1E7Mc5HKycEqzHVAykQZLiLGSZFcIIUh1aChZgG59C1W2GLp2QrTTtAazk6YXr8OFRsGMpdDRBHV/Qyf6zRAKbx4EilGV81ElU3F85DPocA/2hl+jn/wGelKtqXs9gjYHvCA1hthG2Um0VqiSqXDaZahoO7r1fZTHAZNnosP9UPe2SWgT70PNbAhr2PM3dEsbOGzIq4fSqaiSGdDbjIp2oT1+SMTQ/b2oipno7na0rxQVHO4QsDq0U6tch+1aapTlhEg7RLvMVDavB+y46bKgbUgkob0VnUyaCWM+vynzGDzQ5QSny/QTTts0qEw5gY4M4uRwTqBfMqRm97hIsiuEECnK4YbQDGh/B4rmoFvfhvxq6N5rdnn95VAwBZq3YM39+8HH6UQUOhvQrXvQb61HF1ejZpyL8uXh+MhnsRNx9LZnTR3usC88cNApCZZGOzVKa/S+v8C2Z9Gnn4c168MwWaN7GlAHN5vrwr0QU9D9NpSUw+KLUTEb3bgTOg7AuxvRjo3gDUBBMZQ5Ye9ecCTNwbGySdByAN3fOUxMqX9Yh31uVgtJ2yTmlgeVPwlqzkAFS6CvDdr2QOee1HjgZGrinDU4bphIDJL9kEyk+glLgihOkO2G6ce+TExckuwKIcRhlLcQHeuCvgZU3mR0rA/d34IqqIFABfQ1QOc+7Po/mQNaDlOjqpRClZShy6fDvrfRm3+JnrIEq+w0LKcLFl425li01tgtO+Ct32Nvex5mnolVvQROuxTdewC160/Q04EunAytB03iOnUOavIUmFoDCQvdUg/NO2H/LlTlJHRRMXS2o7vbobASVTl9hC4RI0ZlPiTi6Ggvum8PbH3PJK7KMq3SCqpQk+aArwTV1wnN70O8L1WTnAQGktwcOtClsn2HOov5j9InOuco0vduiOzsCiHEhKHyp2G3vIUKTYdoBzj96FiPmapmucBXAP2dZtCCcoOdNIfUADhoug/0dsLuV7Fbd6CmfxjlG/sPZKUUjrLT4O9Ow+5pRr/5a+y6rVB9OlZFLXrB30PzVtSuv4DTi568CPZuQsejUFGDKq9FVVSgy8tgw9Ponh5UKITu64V4FNXRCK7ZY67ZhQRYFirghryS1FurLlO323YA9m5DO981bdi8PpTbCx6FmaTgHlgcOfXDVXaox49/5OFUQoAku0IIMSxVMhfdvBmVNwWdiEDHLig/A+XOR1eeZaaChTtMMmwnU0MGnGbCWSRi6lynLYK6jeit69GhSSMnlQOjZx2u1C31eaAMy2N+kFt5ZXDBrdj9XfDa49j7d8G0OqyyGegP3QzvP4va8WcIlaOrF0HbfvSW35mRwJWnwdzzTMLr80EwD1oj6K5WVHGf6dU7Fu6AGZHs8IDDDMPQ/e2ovgNQ4YayKaAsdDwJTfXo5v2Hag0tdWhy2sCgiaySGibhcKYGZqQ+OuSA2rhJHvuSnCE1u8dFkl0hhBiGUg5w56MdXuhrRCej5iBSoBLVsw9VuvCIx+h4P/rgRkh2QqAM+jtRpTPRTg8cfCd1Yn8YA2/pKwucblPv6nCAjmPbSQhNhsqFWO48LH8ILrwVu+MA+o1fYjfsgUm7sMpno2vOg32vo/ZtMvWygZAZ77t7C+Tnw5zlsPUVqJpseu12tqF3boR928b+DbIU2uVBuf1mLLDLA06vSQKVSWIVSSgsgaLiD6x38B9ZR6MhYVrEkUiawRjxaGrIRHauKeupsb4zISYaSXaFEGIk+dWojvdMv12rG3oaUPmTsZORYffwlMuPmrocHelEN24ClxPtdJsJaUVTRk52B9hJkzglohCOQE8rFE4Gdwje/wM2tqmHDU1FFVShLv6/6PpN6O1/wm5qgPJJWMEi9JyLQStU207obkI7XdCwB2rPMofSOtrN0At/nhn/ax1tR0cdtps08HmqZjCZRPd0gN1qDp+p1A6n5TIJe7AQCiZBqNIkxQ6XuUaZGmedhclh7u59ZTG3f7wjyCDps3s8JNkVQogRKIcbWyfBXwHhNnMoLH8yylOA3fo2SlngKQRvEcrpPfQ4bwFUnwcdu9C9jRCqgkQfo/phcvhbiXomtB+E91+GoslQVguxMLp+I/jfRwXLUJWnoSrnYO94GdV8ALurH+LbIdoLOrXbmIiaRHPXZlTtYvSWl9DaRnl9aB0Czwj1xEqZBDwZH3qLRczUMLcPfCUQyAedgFgvJPpTZR1JiHZCQzvs3YC2k6nJa3aqRVn2JbpDDfTXdZq2ai6vHFIbL6U1MOfi8Y5CnMIk2RVCiKNQwUmmA4MnhI6H0YkIKm8qKg+TwEU7oKceOxkx17vzIG+qSYSLZkJoKjRtMcmlNcJfuZbT7IY6XEM/egugZCG66iDs3QDvvQylk8BfaNq7tu4zSa3LjTVlDrpyDqpxK2rahYMH4rTW6P529B++A06FbtwDsxbBtr+i8/JQk8qOkaQ5UzffYd8Uh0lce7rQkQi010MiNT7YGwJ/vilr6GmGSJe53+lIbUqlJq9l9fm0wwZJHNZ/OIsXlN2S8fGOIHOkZve4SLIrhBBH4y1Bt2xClZ4B4Tbo2Aml8wDMUARfiWmxlbpcR9rRrW+Z0oP8atO7t+qsVMutYXYztTY7sMm4GcyQjJmPsV7o2odOxlC+Iphzqbn8vZeg9QB4nOArhP5uiEfQJR5QcSiqQO/4gxkgUTEP5clDefPQH7kOXvwhNOyFvBBMmg4t+9GNB1HF00b97dDaBrcP5fRAcSHK7odkFPp6INwP9JuuDJYPfF6UZVolacuZGlKR2g21zOcq64ZKDAzOiJmSE22b3Wo7Md6BTVwhKWM4ec+dmyTZFUKIo1BKgSsAiX6UtwDd0wBam/uHu95bhPIWoSMdZiiFOwj501LTwkbiMoe7PqiwBgXocBs0v20S3/KpaPdC2PU6aOtQeUT7XpMo+0JQXmsOUe19E10xG2UpLGVhn/F3sOl52LkNNWs+uu0gKI3ubRpp9amPH0hIuyJmkEYyafJ3y2WSdkVqVzo5eGhLO12HktuBml+tTItdjZlIl00sZdbocYHyppLdJGg5JDVuvMeohRcTniS7QghxLPnToHMnFNZC70HobzHdFo5CeQvNgIpoJ7p1ayrhG+av3KPVrrrzIVCB8hWDrziV+LajOnahSysgYUF/H1ROh656iAPRCMQOmt1idwDq3jDdICpOQ5Wfhq45CLvfRtfvhOlzoe4diISPshJ9ZIxap3ZmUwlePGwSPocD3E7zujZmxzMehmQ49S7/4W/1H3bgLZtonapjTprvcbYl67mozIIZ4x1EhoxzGUMsFuPuu+/mtddeo7Ozk+rqaj7/+c/z0Y9+dMh13/nOd1i7di0/+tGPWLZs2eBj77rrLp599ll8Ph833XQTN9xwQ1qW8kGS7AohxDEohwc7GTN1uL4SdOcu1DGS3cHHegpQpQVoOzFyYjvSz5hoJ7ptGxqFClaBp9CUNPiKzC5u67toFYLG3Sb5njYZIu0Q6YFwN8T6IVgG0X7Yuxmmg6q9AN3fA411kNeEOvdmlL9kDN8NjY70QFcddO+HSHcq6QOdSEJXG7TuN2t1uSCYh8ovMHXBdipJ1mqwPRk6y5JFrU27MTuZKk0R4y5/LP/9ihORSCSorKzkJz/5CVVVVbz88svcfvvtPPXUU0yePBmAffv28eyzz1JaWjrksWvXrmXv3r28+OKLtLa2cv311zNjxgzOPffctMctya4QQoyCClaZEcKhGnRfI/bBDeZ+pUwJgtNnbr5iU6f7wcePdDjtaHylKF+pSZT7GtA99WaIQ95UlMsP5QtNwhXYgW7fAzv2mYQ4VAGVtWhfABXrRofb4d0XoHmfiaX2XHRfFzQ1gbUO7RpjzaPTBZ5Ub13fVPAWgifP/DIAEO+HSAe6Y78ZYdzSkqpNtlO3gd3ibOzIoMDpNIn8wM3hGLGsRWRAomO8I8ig8a3Z9fv93HbbbYNfn3/++UyePJlt27YNJrv33HMPa9as4Wtf+9qQx65bt47777+fUChEKBTiU5/6FE888YQku0IIccrwlaFbNmMFJ0NoGjreB4C2HGC5zeGrZAya3zI1ncWnD5v0Hg9lOU2CmzfVTHPr2YudiJgE3FuCKpkNxbXQvQ/ddQAdOQi9DRBPopVlcsrqs2DXXyCvAhUIw2lnozf9Ad0bhlAgNQXMOfStTIcTZQ1zgCwWhY5UAuv0gr8TfAHQsSG7ncrrh+o5qclxh7dUSw3RyMKEV2ttWq9FwhANQ6QfIrEsW0WOCYXGO4IJq7W1lbq6OmbOnAnA7373O1wu1xFlDV1dXTQ3NzN79uzB+2bPns0f//jHjMQpya4QQoyC2cH1oeP9qPzqQ90XknFzSCxubtrlQVtuaNpsEsHi01EjjQk+njicXlM7rG3obUC3bjF9coNTUKFqVKjaJGThVuiqN5PfHB5o3AtTFsK+v6JPO8+89Xv6Uti9GfrqUm/L20NzT22PnMQNtN7y+qC33XzudJi1Wm5zqC9YBp58cLvNZLjUGGSVzrrDNMvOqHOcc4J1Y0jb/ztje954PM6aNWu46qqrmDFjBn19fXz729/mhz/84RHX9vf3A5CXlzd4X15eHn19fScW8ihJsiuEEKOVPw26dkHx3MG7lMMFjoLBwQwK0LFutE6i7Tg0bgBXEIpnj5j0moELA63H4oc+d/rMwIphfrgpZUHeZFTeZNP5oX0bKKeJwxUAbyHKX2riifejI13Q1mYmsu38C8w8G1VaDW4Pyh0c9bdApw6naVwQjZopbx0HoKsVov1onTqoll8AkRbThcLWAws1592UlarZdWR39qgsM0zC7QW3R4ZKjJdgOarijPGOYkKxbZsvfvGLuFwu/u3f/g0wNblXXHEFU6ZMOeJ6v9/8QtLb24vH4xn8PBAIZCReSXaFEGKUlNOLnYwdtfUYgHLno4rnoJMx6KlHRzvh4OsjN15QynRqUE6TKKpUT9pIp5nCppQZVhGsQnnyj3z4QOeHZAziPRDrgr4G7MGSAg1FNaacoKXJJGf7tsLUeaZN2Rh63So02AlUIgJuC3yl6JLiQzvCrgA6GoP970Jbg2lRZjlMna/lAEfqYJrDSq01yxLEw7sxJBMQj5ueu8lEtlVk5I6KGTBhkl3Ttzp9z31sWmvuvPNOWltb+e///m9cLvNL/GuvvUZjYyM/+9nPAGhvb+f222/npptu4uabb6a0tJTt27fz4Q9/GIDt27cPlj+kmyS7QggxBipQAf2NEKg89rUONxTMMAlSrMvsbB77UYc+jfehLfO1RkNXnSlLQKEKZ6C8hUe+nqMYvMVDnklrjW7ZCMFysLzQrCHSCo17oAK008dYKIV569hbCA6v+ToZTY0TjqFcYZhxOjo5C21r6Os0ta2JuLklk+ZjLD7YySF7pPrsWl5wOcA3MCjDIru3qbNY+WnjHcGEctddd7Fr1y5+9KMf4fUe6g/+4x//mETi0HCVlStXcscddwweQLvyyiv53ve+x7x582htbeWXv/wl9913X0ZilmRXCCHGwl+BbtmMGkWyO0ApNVjmMCbeQhTmhLOO90F/MzrhAK3RXXvRbe+h8iaZcoaj7TQrBYWno7t2oRwKXVoDB8MQ64P2VvCM7a1ErTXQnEpUE+ajskwS6CuAQBkqUIly+VF23CTCdgJ0IrUDmupRayeP3mdYiNHwHsf/W9lqnPvsHjhwgF/84he43W6WL18+eP/XvvY1rrjiiiHXOhwOQqHQYKnCv/zLv3DXXXdx/vnn4/V6Wb16dUY6MYAku0IIMSZKKfCVYre8NfI1+dOGLTc4odd1BSA03dTg2knoO4COdqHDrdBdb0YHF84ascWZcvnBU4D2OqBtJ5TMhINbgOTYf3gqZcosBr60nGin1wyZSMSg6X00CbPb6c0HT3AwGdc69T20nCY5zrYyBnHqcXjGO4IJY9KkSbz33nujuvaFF14Y8rXb7eb+++/n/vvvT0doRyXJrhBCjJFKHQwbjrYT0F2H3bUT5S+DQNWh/rMn6/UtR6oVWeowXM9+dCIMB99EK9M+zHSP8Jtxxe488OSj8qagW7aY/rxNWyA0DXoOQHiMfUotp2lTNrBmnZr9qxwm2VUKnB7whiASg56DpkWbIjWQbaDlmI289S9OWMFUM2VwQhjfPrvZSpJdIYQ4iZTlhIKZZguzvxndstkknfnV5uDZsAYGLaSGLgx87fCYVmNHe72Bw3B2EvoOQrzH1OiiTflAXw/01JvuDhVnoYpOR7dtg5I54KpDFVSZ0oKxiPSg4wMjhhUqWIQOlIDLbV4zEYFYD/S1QU8jxCNDyxVy92eqGA8OB1TMGe8oxClMkl0hhEgDpRQEylGBclNv2733KOOCDxufi3Xo80QEOxlNXeIwdb/eInD6jqjRNbu9Zrf5g7mkTsbRbW+jmzehQjWo4GTTtcEbglgv+MfYlL+gGOX0g8uHdngg3IXqaoT2vZBMmMQbAAd4yiHH3mVWnqD5nvlSN2/wpO/eCzE82dk9HpLsCiFEmilXAIpmH/vC4R6b+qjtBEQ7oXe/SZ4tJ+RVj6o2WDlcUDIf3boVHWlHaW3ameVXmw4KY+2IkIyndm6bUIlwqowBCB7Z1UFZrsFhElhuU/5guVJfu4bU/mYDrW10rB8iPdDXAq27IdaXKs0Q46JkOlal7OyKkUmyK4QQWUBZTvCVgK/ElL4m49CzD7trl0mm86tRRzmooywXFJ2O7ngP7S83/XvjfaiKJSe8KznSfpDWpiev6cZw2MCMgTKHZHyU7dhOHUPW6nGDpxiYKPWipyh/4bGvyRFqoB4/Tc+dqyTZFUKILKQcLiiYkZrY1gOdO7HtuElcHV4zfc3pBYf5qCynqf8NzUB310H5mdDwOnrfi+ixjlt1OMFdYDpAuEPmuYed8pbqSXsSxyULIcRYSbIrhBBZTrnzoHiuSXy1feiQWCIM0W5IhrETUVTxHDMaODgJevbC1POgt2HMZQw6GTPP23cQEmEzGhkw9cap+mM+8NFypsoZ3KZVlMMDDrepNRbiRLgC5v+BCUFqdo+HJLtCCJFDlLJSu7o+4LC3d5NxdPs28BSh8qei7Rh070GFasb+GiPcr+3EYcMiDrvZSbQdh0QUkhFIhs1EuWTCdI0Q4kQEKiZQsiuOhyS7QggxASiHC1V6Brq3Abt5I6p4bioBfteUPYyF5Txsd9bclFKpgRZOGGazNnf3jITIoHGeoJatJNkVQogJRAWrwFeMbnvHDL3In44Z7jAGdtKUSsR7IdwKdgz7sG4ESjlSwyzyzVvM0pZLiJMod5PSdJFkVwghJhjl8KDKFqF76s3O7gm0/1IKUE7T7SFVl6uVZbow9DdCIobO4R0jMf6Ur8T84ibECCTZFUKICUrlTUHlTTmh5xhsL2bHB2/KHmgpZg6naTt+cgIWYjhj7ROdzVRq6Ey6njtHSbIrhBDiuI2mvZjs6wohxpMku0IIIYQQWUFajx2P3N2zFkIIIYQQE57s7AohhBBCZANpPXZcZGdXCCGEEELkLNnZFUIIIYTIClKzezxkZ1cIIYQQQuQs2dkVQgghhMgGUrN7XGRnVwghhBBC5CzZ2RVCCCGEyApSs3s8ZGdXCCGEEELkLNnZFUIIIYTIBoo01uym52lPBbKzK4QQQgghcpbs7AohhBBCZAWp2T0esrMrhBBCCCFyluzsCiGEEEJkBQtUuvYpc3f/M3dXJoQQQgghJjzZ2RVCCCGEyApSs3s8JNkVQgghhMgGMi74uEgZgxBCCCGEyFmysyuEEEIIkRWkjOF45Eyym0wmAWhsbBznSIQQQgiRKwbyioE8Yzw1NjVn5XOPt5xJdltaWgD49Kc/Pc6RCCGEECLXtLS0UF1dPS6vHQwGCYVCfPqGW9P6OqFQiGAwmNbXGA9Ka63HO4iTIRKJsHXrVkpLS3E4HOMdjhBCCCFyQDKZpKWlhXnz5uH1esctjs7OTnp7e9P6GsFgkIKCgrS+xnjImWRXCCGEEEKID5JuDEIIIYQQImdJsiuEEEIIIXKWJLtCCCGEECJnSbIrhBBCCCFyliS7QgghhBAiZ0myK4QQQgghcpYkuxlQV1fH/PnzWbNmzbD/fu3atcydO5dFixYN3urr6zMcZXoca+0A27Zt49Of/jSLFi1i2bJlPProoxmMMH2OtfabbrppyJ/5vHnzuPzyyzMcZXoca+2xWIyvfvWrLFu2jLPPPptbbrmFpqamDEeZHsdae3d3N1/60pdYunQpS5cuZe3atRmOMD2uu+465s+fP/jf88UXXzzsdVpr/vM//5NzzjmHc845h//4j/8g2ztgjnbtr7/+Otdddx2LFy/mggsuyHCU6TPa9f/gBz/gsssuY9GiRVxwwQX84Ac/yHCkYqLKmQlqp7J77rmH+fPnH/Waj33sY3zrW9/KUESZc6y1t7e3c9NNN/Gv//qvXHLJJcRisZxJeo619g/+RX/ddddxzjnnpDusjDjW2h999FE2b97Mk08+SV5eHl/5yle49957+c53vpPBKNPjWGu///77CYfDvPDCC7S1tfHZz36Wqqoqrr766gxGmR5f/epX+dSnPnXUa37xi1/w/PPPs379epRS3HDDDUyZMoVrr702Q1Gmx2jW7vf7ufrqq7nsssv4/ve/n6HIMmM069da8+///u/U1tayb98+/uEf/oHKykouvfTSDEUpJirZ2U2z3/72t+Tl5bF06dLxDiXjRrP2H//4xyxfvpwrrrgCt9tNMBhkxowZGYwyPcb6575//342bNjAJz7xiTRHln6jWfv+/ftZvnw5JSUleDweLr30Unbs2JHBKNNjNGt/4YUXuOmmm/D5fEyePJmVK1fy61//OoNRjq9169Zx4403UlFRQXl5OTfccANPPPHEeIeVEQsWLODKK69kypQp4x3KuFi9ejVz587F6XRSU1PDhRdeyMaNG8c7LDEBSLKbRr29vTzREbUfAAAItElEQVT00EPccccdx7z2xRdf5Oyzz+bSSy/l8ccfz0B06TXatW/evJlQKMSqVatYunQpt9xyCw0NDRmKMj3G8uc+YN26dSxZsiTrfwiOdu0rV65k48aNNDU1EQ6Heeqppzj33HMzFGV6HM+fO5jdrlxI9AEeeOABzjnnHFatWsUbb7wx7DU7duxg9uzZg1/Pnj07J9Y/mrXnsrGuX2vNhg0bmDlzZgaiExOdJLtp9OCDD3L11VdTWVl51Os+9rGP8cwzz/Daa69x77338t3vfpenn346Q1Gmx2jX3tTUxLp16/jyl7/MSy+9xOTJk/nCF76QoSjTY7RrP9z69eu56qqr0hhVZox27dOnT6eqqopzzz2XxYsXs2vXLj73uc9lKMr0GO3aP/KRj/DII4/Q29vL3r17+fWvf004HM5QlOmzZs0ann/+ef70pz9xzTXXcMstt7Bv374jruvv7ycYDA5+nZeXR39/f1bX7Y527bnqeNa/du1abNvOifIdceqTZDdN3n33XV577TU++9nPHvPamTNnUl5ejsPh4Mwzz+T666/n2WefTX+QaTKWtXs8HlasWMGCBQvweDx87nOfY9OmTfT09KQ/0DQYy9oHbNiwgdbW1hEPdWSLsaz9rrvuIhqN8sYbb7B582ZWrFjB6tWr0x9kmoxl7V/5ylfweDxcfPHF/NM//ROXXnopFRUV6Q8yzRYuXEgwGMTtdnPVVVdx5pln8vLLLx9xnd/vp6+vb/Dr3t5e/H4/SqlMhntSjXbtuWqs63/sscdYt24djzzyCG63O4ORiolKDqilyRtvvMGBAwc4//zzAbObkUwmueqqq0ZVn5bNuxxjWXttbe2Qrwd+4GXr+o/nz33dunWsWLGCQCCQyVBPurGs/b333uP222+noKAAMIfzHnroIdrb2ykqKsp47CdqLGsvKCjggQceGPz6v/7rv1iwYEFG480EpdSw/x/PmjWL7du3D655+/btzJo1K9PhpdVIa58ojrb+X/3qVzzyyCP89Kc/zYlf8kSW0CIt+vv7dXNz8+Dtm9/8pr7tttt0W1vbEdf+4Q9/0J2dndq2bb1lyxa9fPly/Zvf/GYcoj45xrL2V199VS9ZskS/8847OhaL6W984xv62muvHYeoT46xrF1rrcPhsF68eLF+9dVXMxzpyTeWtd9xxx36n//5n3V3d7eOxWL6e9/7nl6+fPk4RH1yjGXte/fu1e3t7TqRSOiXXnpJn3322fr9998fh6hPnq6uLv3KK6/oSCSi4/G4Xr9+vV64cKHetWvXEdc+/vjj+pJLLtGNjY26sbFRf/zjH9ePP/74OER9coxl7clkUkciEf3SSy/p8847T0ciER2NRsch6pNnLOtfv369XrZsmd65c+c4RComMtnZTROfz4fP5xv82u/343a7KSoqYsOGDaxevZpNmzYB8Mwzz3DnnXcSi8UoLy9n9erVWV2/OZa1L126lM9//vPcfPPNRCIRFi9ePGTXK9uMZe0Azz//PHl5eXzoQx8aj3BPqrGs/Ytf/CJf//rXueiii4jH48yaNYuHH354vEI/YWNZ+9atW7nvvvvo6elh2rRpfOtb38r6nc1EIsGDDz7I7t27cTgc1NTU8PDDD1NTU3PE+letWkV9ff1gT+mVK1eyatWq8Qz/hIxl7W+++SbXX3/94GMXLFjA2WefzU9+8pPxCv+EjWX9Dz74IJ2dnaxcuXLw8Zdffjn33HPPeIUvJgil9QR+r0UIIYQQQuQ0OaAmhBBCCCFyliS7QgghhBAiZ0myK4QQQgghcpYku0IIIYQQImdJsiuEEEIIIXKWJLtCCCGEECJnSbIrhBBCCCFylgyVEEJkpeuuu46//vWvQ+5bvnw5P/zhD4/r+e644w4AvvnNb55wbEIIIU4dkuwKIbLWjTfeyI033jj4tdvtHvNzJJPJkxmSEEKIU4yUMQghspbf76e0tHTwFgqFAPjzn//M5Zdfzrx587jooot4+umnBx+zf/9+amtr+f3vf88nP/lJFixYwJo1a3jiiSd44oknqK2tpba2lqamJk4//XR27do15DVvueUWvvGNb2R0nUIIIY6f7OwKIXJKQ0MDt956KzfffDOXXXYZf/nLX/jSl77E1KlTWbBgweB1a9eu5c4776SyspKysjIGJqffeeedAJSWlrJs2TLWr1/PF77wBQA6Ojr485//zM9//vPML0wIIcRxkZ1dIUTW+v73v8+iRYsGby+++CI/+9nPmDt3LrfddhvTp0/nM5/5DBdddBGPPvrokMf+4z/+I8uWLWP69OkEAgG8Xi9er3dwlxjgyiuv5Omnnx5MhJ955hmmTp3KvHnzMr5WIYQQx0eSXSFE1lq1ahXr1q0bvJ1zzjns3r2bhQsXDrnujDPOYPfu3UPumzNnzjGff8WKFXR1dbFhwwYAnnzyST7xiU+cvAUIIYRIOyljEEJkrVAoRHV19ZD7BnZhj8Xr9Y7qmksuuYQnn3yS8vJy3nrrLR588MHjilUIIcT4kGRXCJFTampqBndiB2zevJmampqjPs7pdBKNRo+4/6qrruLWW2+lsLCQs846i8rKypMarxBCiPSSMgYhRE659tpr2bp1K2vXrmXPnj089thjPPfcc1x//fVHfVxVVRXbt2/nwIEDtLe3D96/ZMkSCgoK+J//+R8pYRBCiCwkya4QIqdMmjSJhx9+mOeee47LL7+cRx99lPvuu++IOt4PWrlyJaFQiI9//OMsXbp0yL+74oorcDgcXHzxxekMXQghRBooPdoCNyGEmKDuvvtuenp6eOCBB8Y7FCGEEGMkO7tCCDGCvr4+NmzYwPr161m1atV4hyOEEOI4yAE1IYQYwb333sszzzzDNddcw1lnnTXe4QghhDgOUsYghBBCCCFylpQxCCGEEEKInCXJrhBCCCGEyFmS7AohhBBCiJwlya4QQgghhMhZkuwKIYQQQoic9f8BNAn7zLeuBzIAAAAASUVORK5CYII= "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>The PDP is the average of all ICE curves on the plot, so the PDP above represents the average change in the predicted AV percentile over the range of forty times.</p> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h2 id="Centered-ICE-Plots">Centered ICE Plots<a class="anchor-link" href="#Centered-ICE-Plots">¶</a></h2><p>One drawback with our previous ICE plots is that the stacked nature of the lines can make it difficult to observe the differences between the ICE curves. To make it easier to spot those differences we can center or "pinch" the curves at a specific feature value. Typically the minimum is a good centering point. With these centered ICE plots we observe the relative change of the predictions with respect to the predictions at the centered value.</p> <p>To center our ICE curves at the minimum Forty value we just set <code>centered</code> to <code>True</code>.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [56]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">ice_plot</span><span class="p">(</span><span class="n">forty_ice_df</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">color_by</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap2</span><span class="p">,</span> <span class="n">plot_pdp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pdp_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">'c'</span><span class="p">:</span> <span class="s1">'k'</span><span class="p">,</span> <span class="s1">'linewidth'</span><span class="p">:</span> <span class="mi">5</span><span class="p">},</span> <span class="n">centered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Wt'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Pred. AV </span><span class="si">%i</span><span class="s1">le (centered)'</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Forty'</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Note that each line above starts at 0. The y-axis now represents the difference in each player's prediction relative to their prediction at the minimum forty yard dash time of 4.47 seconds.</p> <p>Lets take a look at the ICE plots for all our features. To make that easier to do, I created a helper function that can plot out all the ICE plots for each feature. It also adds a rug plot at the bottom of each ICE plot to display information about the distribution of the data.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [57]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_ice_grid</span><span class="p">(</span><span class="n">dict_of_ice_dfs</span><span class="p">,</span> <span class="n">data_df</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">ax_ylabel</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">subplots_kws</span><span class="o">=</span><span class="p">{},</span> <span class="n">rug_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'color'</span><span class="p">:</span><span class="s1">'k'</span><span class="p">},</span> <span class="o">**</span><span class="n">ice_plot_kws</span><span class="p">):</span> <span class="sd">"""A function that plots ICE plots for different features in a grid."""</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">ncols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">sharex</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="n">sharey</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kws</span><span class="p">)</span> <span class="c1"># for each feature plot the ice curves and add a rug at the bottom of the </span> <span class="c1"># subplot</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span> <span class="n">ice_plot</span><span class="p">(</span><span class="n">dict_of_ice_dfs</span><span class="p">[</span><span class="n">f</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">ice_plot_kws</span><span class="p">)</span> <span class="c1"># add the rug</span> <span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">data_df</span><span class="p">[</span><span class="n">f</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">hist</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rug_kws</span><span class="o">=</span><span class="n">rug_kws</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'feature = '</span> <span class="o">+</span> <span class="n">f</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ax_ylabel</span><span class="p">)</span> <span class="n">sns</span><span class="o">.</span><span class="n">despine</span><span class="p">()</span> <span class="c1"># get rid of blank plots</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">),</span> <span class="n">nrows</span><span class="o">*</span><span class="n">ncols</span><span class="p">):</span> <span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span> <span class="k">return</span> <span class="n">fig</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [58]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create dict of ICE data for grid of ICE plots</span> <span class="n">train_ice_dfs</span> <span class="o">=</span> <span class="p">{</span><span class="n">feat</span><span class="p">:</span> <span class="n">ice</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_X_imp_df</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="n">feat</span><span class="p">,</span> <span class="n">predict</span><span class="o">=</span><span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">)</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">features</span><span class="p">}</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plot_ice_grid</span><span class="p">(</span><span class="n">train_ice_dfs</span><span class="p">,</span> <span class="n">train_X_imp_df</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">ax_ylabel</span><span class="o">=</span><span class="s1">'Pred. AV </span><span class="si">%i</span><span class="s1">le'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">plot_pdp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pdp_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">'c'</span><span class="p">:</span> <span class="s1">'red'</span><span class="p">,</span> <span class="s1">'linewidth'</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'dimgray'</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span> <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'ICE plots (training data)'</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.89</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [59]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plot_ice_grid</span><span class="p">(</span><span class="n">train_ice_dfs</span><span class="p">,</span> <span class="n">train_X_imp_df</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">ax_ylabel</span><span class="o">=</span><span class="s1">'Pred AV </span><span class="si">%i</span><span class="s1">le (centered)'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">2</span><span class="p">,</span> <span class="n">plot_points</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot_pdp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pdp_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">"c"</span><span class="p">:</span> <span class="s2">"red"</span><span class="p">,</span> <span class="s2">"linewidth"</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'dimgray'</span><span class="p">,</span> <span class="n">centered</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span><span class="mi">16</span><span class="p">))</span> <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span> <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Centered ICE plots (training data)'</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h2 id="Two-dimensional-PDPs">Two-dimensional PDPs<a class="anchor-link" href="#Two-dimensional-PDPs">¶</a></h2><p>We can also create plot the PDPs for 2 features at once. This allows us to better understand interactions between the features and how they impact the predictions. We'll use the the <code>pdpbox</code> library to create 2-D PDPs.</p> <p><strong>NOTE</strong> <code>pdpbox</code> can also create the same kind of ice plots that we created with <code>pycebox</code> above. However <code>pycebox</code> doesn't have support for 2-D PDPs.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [60]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">itertools</span> <span class="kn">from</span> <span class="nn">pdpbox</span> <span class="k">import</span> <span class="n">pdp</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>I'm just going to jump into creating a grid of 2-D PDPs and explain whats happening in comments of the code below.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [61]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.text</span> <span class="k">import</span> <span class="n">Text</span> <span class="k">def</span> <span class="nf">plot_2d_pdp_grid</span><span class="p">(</span><span class="n">pdp_inters</span><span class="p">,</span> <span class="n">feature_pairs</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span> <span class="n">xaxis_font_size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">yaxis_font_size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">contour_line_fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">tick_labelsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">x_quantile</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">plot_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subplots_kws</span><span class="o">=</span><span class="p">{}):</span> <span class="sd">"""Plots a grid of 2D PDP plots."""</span> <span class="c1"># create our subplots to plot our PDPs on</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">ncols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kws</span><span class="p">)</span> <span class="c1"># for each feature pair, plot the 2-D pdp</span> <span class="k">for</span> <span class="n">pdp_inter</span><span class="p">,</span> <span class="n">feat_pair</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">pdp_inters</span><span class="p">,</span> <span class="n">feature_pairs</span><span class="p">,</span> <span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span> <span class="c1"># use pdpbox's _pdp_contour_plot function to actually plot the 2D pdp</span> <span class="n">pdp</span><span class="o">.</span><span class="n">_pdp_contour_plot</span><span class="p">(</span><span class="n">pdp_inter</span><span class="p">,</span> <span class="n">feat_pair</span><span class="p">,</span> <span class="n">x_quantile</span><span class="o">=</span><span class="n">x_quantile</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_params</span><span class="o">=</span><span class="n">plot_params</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># adjust some font sizes</span> <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="n">tick_labelsize</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="n">xaxis_font_size</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="n">yaxis_font_size</span><span class="p">)</span> <span class="c1"># set the contour line fontsize</span> <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_children</span><span class="p">():</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">child</span><span class="p">,</span> <span class="n">Text</span><span class="p">):</span> <span class="n">child</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">contour_line_fontsize</span><span class="p">)</span> <span class="c1"># get rid of empty subplots</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pdp_inters</span><span class="p">),</span> <span class="n">nrows</span><span class="o">*</span><span class="n">ncols</span><span class="p">):</span> <span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span> <span class="k">return</span> <span class="n">fig</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [62]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get each possible feature pair combination</span> <span class="n">feature_pairs</span> <span class="o">=</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">feat_pair</span><span class="p">)</span> <span class="k">for</span> <span class="n">feat_pair</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span> <span class="c1"># we will only plot the feature iteractions that invlove either Forty or Wt</span> <span class="c1"># just to avoid making soooo many plots</span> <span class="n">forty_wt_feat_pairs</span> <span class="o">=</span> <span class="p">[</span><span class="n">fp</span> <span class="k">for</span> <span class="n">fp</span> <span class="ow">in</span> <span class="n">feature_pairs</span> <span class="k">if</span> <span class="s1">'Forty'</span> <span class="ow">in</span> <span class="n">fp</span> <span class="ow">or</span> <span class="s1">'Wt'</span> <span class="ow">in</span> <span class="n">fp</span><span class="p">]</span> <span class="c1"># now calculate the data for the pdp interactions</span> <span class="c1"># we can do that with pdpbox's pdp_interact function</span> <span class="c1"># in the current development version on github, parallelization is supported</span> <span class="c1"># but it didn't work for me so I resorted to using that multiprocess helper</span> <span class="c1"># function from before</span> <span class="n">train_feat_inters</span> <span class="o">=</span> <span class="n">multiproc_iter_func</span><span class="p">(</span><span class="n">N_JOBS</span><span class="p">,</span> <span class="n">forty_wt_feat_pairs</span><span class="p">,</span> <span class="n">pdp</span><span class="o">.</span><span class="n">pdp_interact</span><span class="p">,</span> <span class="s1">'features'</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span> <span class="n">train_X</span><span class="o">=</span><span class="n">train_X_imp_df</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [63]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># and now plot a grid of PDP interaction plots</span> <span class="c1"># NOTE that the contour colors do not represent the same values</span> <span class="c1"># across the different subplots</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plot_2d_pdp_grid</span><span class="p">(</span><span class="n">train_feat_inters</span><span class="p">,</span> <span class="n">forty_wt_feat_pairs</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span> <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'PDP Interaction Plots (training data)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span> <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.95</span><span class="p">);</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div 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 "> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <h1 id="LIME">LIME<a class="anchor-link" href="#LIME">¶</a></h1><p>Local interpretable model-agnostic explanations (LIME) allow us to explain individual predictions for "black box" models by creating local, interpretable, surrogate models. We fit a local model using the following recipe (which I copied from <a href="https://twitter.com/ChristophMolnar">Christop Molnar's</a> great book, <a href="https://christophm.github.io/interpretable-ml-book/lime.html">Interpretable Machine Learning</a>):</p> <blockquote><ul> <li>Choose your instance of interest for which you want to have an explanation of its black box prediction.</li> <li>Perturb your dataset and get the black box predictions for these new points.</li> <li>Weight the new samples by their proximity to the instance of interest.</li> <li>Fit a weighted, interpretable model on the dataset with the variations.</li> <li>Explain prediction by interpreting the local model.</li> </ul> </blockquote> <p>There are different packages that implement LIME (including <code>eli5</code> and another package I just discovered called <code>Skater</code>). Here we will use the original <code>lime</code> package created by the authors of the LIME <a href="https://arxiv.org/abs/1602.04938">paper</a>.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [64]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">lime</span> <span class="kn">from</span> <span class="nn">lime.lime_tabular</span> <span class="k">import</span> <span class="n">LimeTabularExplainer</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Lets create our LIME explainer and explain an instance from our test set.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [65]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the explainer by passing our training data, </span> <span class="c1"># setting the correct modeling mode, pass in feature names and</span> <span class="c1"># make sure we don't discretize the continuous features</span> <span class="n">explainer</span> <span class="o">=</span> <span class="n">LimeTabularExplainer</span><span class="p">(</span><span class="n">train_X_imp_df</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'regression'</span><span class="p">,</span> <span class="n">feature_names</span><span class="o">=</span><span class="n">features</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span> <span class="n">discretize_continuous</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [66]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_X_imp_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">test_X_imp</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">features</span><span class="p">)</span> <span class="c1"># the number of features to include in our predictions</span> <span class="n">num_features</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">)</span> <span class="c1"># the index of the instance we want to explaine</span> <span class="n">exp_idx</span> <span class="o">=</span> <span class="mi">2</span> <span class="n">exp</span> <span class="o">=</span> <span class="n">explainer</span><span class="o">.</span><span class="n">explain_instance</span><span class="p">(</span><span class="n">test_X_imp_df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">exp_idx</span><span class="p">,:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">,</span> <span class="n">num_features</span><span class="o">=</span><span class="n">num_features</span><span class="p">)</span> </pre></div> </div> </div> </div> </div> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt"> </div> <div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Cool, now we have our explanation, lets inspect it.</p> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [67]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the prediction made by the local surrogate model</span> <span class="n">exp</span><span class="o">.</span><span class="n">local_pred</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[67]:</div> <div class="output_text output_subarea output_execute_result"> <pre>0.40375409629113346</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [68]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the bias term for the local explanation</span> <span class="n">exp</span><span class="o">.</span><span class="n">intercept</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt output_prompt">Out[68]:</div> <div class="output_text output_subarea output_execute_result"> <pre>0.4044365285762989</pre> </div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [69]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># a plot of the weights for each feature</span> <span class="n">exp</span><span class="o">.</span><span class="n">as_pyplot_figure</span><span class="p">();</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_png output_subarea "> <img 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"> </img></div> </div> </div> </div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt">In [70]:</div> <div class="inner_cell"> <div class="input_area"> <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># and a prettier output that we can view in the notebook</span> <span class="n">exp</span><span class="o">.</span><span class="n">show_in_notebook</span><span class="p">()</span> </pre></div> </div> </div> </div> <div class="output_wrapper"> <div class="output"> <div class="output_area"><div class="prompt"></div> <div class="output_html rendered_html output_subarea "> <html> <meta content="text/html; charset=utf-8" http-equiv="content-type"> <head><script>var lime = /******/ (function(modules) { // webpackBootstrap /******/ // The module cache /******/ var installedModules = {}; /******/ /******/ // The require function /******/ function __webpack_require__(moduleId) { /******/ /******/ // Check if 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return __webpack_require__(0); /******/ }) /************************************************************************/ /******/ ([ /* 0 */ /***/ (function(module, exports, __webpack_require__) { /* WEBPACK VAR INJECTION */(function(global) {'use strict'; Object.defineProperty(exports, "__esModule", { value: true }); exports.PredictedValue = exports.PredictProba = exports.Barchart = exports.Explanation = undefined; var _explanation = __webpack_require__(1); var _explanation2 = _interopRequireDefault(_explanation); var _bar_chart = __webpack_require__(3); var _bar_chart2 = _interopRequireDefault(_bar_chart); var _predict_proba = __webpack_require__(6); var _predict_proba2 = _interopRequireDefault(_predict_proba); var _predicted_value = __webpack_require__(7); var _predicted_value2 = _interopRequireDefault(_predicted_value); function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } if (!global._babelPolyfill) { __webpack_require__(8); } __webpack_require__(304); exports.Explanation = _explanation2.default; exports.Barchart = _bar_chart2.default; exports.PredictProba = _predict_proba2.default; exports.PredictedValue = _predicted_value2.default; //require('style-loader'); /* WEBPACK VAR INJECTION */}.call(exports, (function() { return this; }()))) /***/ }), /* 1 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; Object.defineProperty(exports, "__esModule", { value: true }); var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }(); var _d2 = __webpack_require__(2); var _d3 = _interopRequireDefault(_d2); var _bar_chart = __webpack_require__(3); var _bar_chart2 = _interopRequireDefault(_bar_chart); var _lodash = __webpack_require__(4); function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } var Explanation = function () { function Explanation(class_names) { _classCallCheck(this, Explanation); this.names = class_names; if (class_names.length < 10) { this.colors = _d3.default.scale.category10().domain(this.names); this.colors_i = _d3.default.scale.category10().domain((0, _lodash.range)(this.names.length)); } else { this.colors = _d3.default.scale.category20().domain(this.names); this.colors_i = _d3.default.scale.category20().domain((0, _lodash.range)(this.names.length)); } } // exp: [(feature-name, weight), ...] // label: int // div: d3 selection Explanation.prototype.show = function show(exp, label, div) { var svg = div.append('svg').style('width', '100%'); var colors = ['#5F9EA0', this.colors_i(label)]; var names = ['NOT ' + this.names[label], this.names[label]]; if (this.names.length == 2) { colors = [this.colors_i(0), this.colors_i(1)]; names = this.names; } var plot = new _bar_chart2.default(svg, exp, true, names, colors, true, 10); svg.style('height', plot.svg_height); }; // exp has all ocurrences of words, with start index and weight: // exp = [('word', 132, -0.13), ('word3', 111, 1.3) Explanation.prototype.show_raw_text = function show_raw_text(exp, label, raw, div) { var opacity = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : true; //let colors=['#5F9EA0', this.colors(this.exp['class'])]; var colors = ['#5F9EA0', this.colors_i(label)]; if (this.names.length == 2) { colors = [this.colors_i(0), this.colors_i(1)]; } var word_lists = [[], []]; var max_weight = -1; var _iteratorNormalCompletion = true; var _didIteratorError = false; var _iteratorError = undefined; try { for (var _iterator = exp[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { var _step$value = _slicedToArray(_step.value, 3), word = _step$value[0], start = _step$value[1], weight = _step$value[2]; if (weight > 0) { word_lists[1].push([start, start + word.length, weight]); } else { word_lists[0].push([start, start + word.length, -weight]); } max_weight = Math.max(max_weight, Math.abs(weight)); } } catch (err) { _didIteratorError = true; _iteratorError = err; } finally { try { if (!_iteratorNormalCompletion && _iterator.return) { _iterator.return(); } } finally { if (_didIteratorError) { throw _iteratorError; } } } if (!opacity) { max_weight = 0; } this.display_raw_text(div, raw, word_lists, colors, max_weight, true); }; // exp is list of (feature_name, value, weight) Explanation.prototype.show_raw_tabular = function show_raw_tabular(exp, label, div) { div.classed('lime', true).classed('table_div', true); var colors = ['#5F9EA0', this.colors_i(label)]; if (this.names.length == 2) { colors = [this.colors_i(0), this.colors_i(1)]; } var table = div.append('table'); var thead = table.append('tr'); thead.append('td').text('Feature'); thead.append('td').text('Value'); thead.style('color', 'black').style('font-size', '20px'); var _iteratorNormalCompletion2 = true; var _didIteratorError2 = false; var _iteratorError2 = undefined; try { for (var _iterator2 = exp[Symbol.iterator](), _step2; !(_iteratorNormalCompletion2 = (_step2 = _iterator2.next()).done); _iteratorNormalCompletion2 = true) { var _step2$value = _slicedToArray(_step2.value, 3), fname = _step2$value[0], value = _step2$value[1], weight = _step2$value[2]; var tr = table.append('tr'); tr.style('border-style', 'hidden'); tr.append('td').text(fname); tr.append('td').text(value); if (weight > 0) { tr.style('background-color', colors[1]); } else if (weight < 0) { tr.style('background-color', colors[0]); } else { tr.style('color', 'black'); } } } catch (err) { _didIteratorError2 = true; _iteratorError2 = err; } finally { try { if (!_iteratorNormalCompletion2 && _iterator2.return) { _iterator2.return(); } } finally { if (_didIteratorError2) { throw _iteratorError2; } } } }; Explanation.prototype.hexToRgb = function hexToRgb(hex) { var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex); return result ? { r: parseInt(result[1], 16), g: parseInt(result[2], 16), b: parseInt(result[3], 16) } : null; }; Explanation.prototype.applyAlpha = function applyAlpha(hex, alpha) { var components = this.hexToRgb(hex); return 'rgba(' + components.r + "," + components.g + "," + components.b + "," + alpha.toFixed(3) + ")"; }; // sord_lists is an array of arrays, of length (colors). if with_positions is true, // word_lists is an array of [start,end] positions instead Explanation.prototype.display_raw_text = function display_raw_text(div, raw_text) { var word_lists = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : []; var colors = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : []; var max_weight = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : 1; var positions = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : false; div.classed('lime', true).classed('text_div', true); div.append('h3').text('Text with highlighted words'); var highlight_tag = 'span'; var text_span = div.append('span').style('white-space', 'pre-wrap').text(raw_text); var position_lists = word_lists; if (!positions) { position_lists = this.wordlists_to_positions(word_lists, raw_text); } var objects = []; var _iteratorNormalCompletion3 = true; var _didIteratorError3 = false; var _iteratorError3 = undefined; try { var _loop = function _loop() { var i = _step3.value; position_lists[i].map(function (x) { return objects.push({ 'label': i, 'start': x[0], 'end': x[1], 'alpha': max_weight === 0 ? 1 : x[2] / max_weight }); }); }; for (var _iterator3 = (0, _lodash.range)(position_lists.length)[Symbol.iterator](), _step3; !(_iteratorNormalCompletion3 = (_step3 = _iterator3.next()).done); _iteratorNormalCompletion3 = true) { _loop(); } } catch (err) { _didIteratorError3 = true; _iteratorError3 = err; } finally { try { if (!_iteratorNormalCompletion3 && _iterator3.return) { _iterator3.return(); } } finally { if (_didIteratorError3) { throw _iteratorError3; } } } objects = (0, _lodash.sortBy)(objects, function (x) { return x['start']; }); var node = text_span.node().childNodes[0]; var subtract = 0; var _iteratorNormalCompletion4 = true; var _didIteratorError4 = false; var _iteratorError4 = undefined; try { for (var _iterator4 = objects[Symbol.iterator](), _step4; !(_iteratorNormalCompletion4 = (_step4 = _iterator4.next()).done); _iteratorNormalCompletion4 = true) { var obj = _step4.value; var word = raw_text.slice(obj.start, obj.end); var start = obj.start - subtract; var end = obj.end - subtract; var match = document.createElement(highlight_tag); match.appendChild(document.createTextNode(word)); match.style.backgroundColor = this.applyAlpha(colors[obj.label], obj.alpha); var after = node.splitText(start); after.nodeValue = after.nodeValue.substring(word.length); node.parentNode.insertBefore(match, after); subtract += end; node = after; } } catch (err) { _didIteratorError4 = true; _iteratorError4 = err; } finally { try { if (!_iteratorNormalCompletion4 && _iterator4.return) { _iterator4.return(); } } finally { if (_didIteratorError4) { throw _iteratorError4; } } } }; Explanation.prototype.wordlists_to_positions = function wordlists_to_positions(word_lists, raw_text) { var ret = []; var _iteratorNormalCompletion5 = true; var _didIteratorError5 = false; var _iteratorError5 = undefined; try { for (var _iterator5 = word_lists[Symbol.iterator](), _step5; !(_iteratorNormalCompletion5 = (_step5 = _iterator5.next()).done); _iteratorNormalCompletion5 = true) { var words = _step5.value; if (words.length === 0) { ret.push([]); continue; } var re = new RegExp("\\b(" + words.join('|') + ")\\b", 'gm'); var temp = void 0; var list = []; while ((temp = re.exec(raw_text)) !== null) { list.push([temp.index, temp.index + temp[0].length]); } ret.push(list); } } catch (err) { _didIteratorError5 = true; _iteratorError5 = err; } finally { try { if (!_iteratorNormalCompletion5 && _iterator5.return) { _iterator5.return(); } } finally { if (_didIteratorError5) { throw _iteratorError5; } } } return ret; }; return Explanation; }(); exports.default = Explanation; /***/ }), /* 2 */ /***/ (function(module, exports, __webpack_require__) { var __WEBPACK_AMD_DEFINE_FACTORY__, __WEBPACK_AMD_DEFINE_RESULT__;!function() { var d3 = { version: "3.5.17" }; var d3_arraySlice = [].slice, d3_array = function(list) { return d3_arraySlice.call(list); }; var d3_document = this.document; function d3_documentElement(node) { return node && (node.ownerDocument || node.document || node).documentElement; } function d3_window(node) { return node && (node.ownerDocument && node.ownerDocument.defaultView || node.document && node || node.defaultView); } if (d3_document) { try { d3_array(d3_document.documentElement.childNodes)[0].nodeType; } catch (e) { d3_array = function(list) { var i = list.length, array = new Array(i); while (i--) array[i] = list[i]; return array; }; } } if (!Date.now) Date.now = function() { return +new Date(); }; if (d3_document) { try { d3_document.createElement("DIV").style.setProperty("opacity", 0, ""); } catch (error) { var d3_element_prototype = this.Element.prototype, d3_element_setAttribute = d3_element_prototype.setAttribute, d3_element_setAttributeNS = d3_element_prototype.setAttributeNS, d3_style_prototype = this.CSSStyleDeclaration.prototype, d3_style_setProperty = d3_style_prototype.setProperty; d3_element_prototype.setAttribute = function(name, value) { d3_element_setAttribute.call(this, name, value + ""); }; d3_element_prototype.setAttributeNS = function(space, local, value) { d3_element_setAttributeNS.call(this, space, local, value + ""); }; d3_style_prototype.setProperty = function(name, value, priority) { d3_style_setProperty.call(this, name, value + "", priority); }; } } d3.ascending = d3_ascending; function d3_ascending(a, b) { return a < b ? -1 : a > b ? 1 : a >= b ? 0 : NaN; } d3.descending = function(a, b) { return b < a ? -1 : b > a ? 1 : b >= a ? 0 : NaN; }; d3.min = function(array, f) { var i = -1, n = array.length, a, b; if (arguments.length === 1) { while (++i < n) if ((b = array[i]) != null && b >= b) { a = b; break; } while (++i < n) if ((b = array[i]) != null && a > b) a = b; } else { while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) { a = b; break; } while (++i < n) if ((b = f.call(array, array[i], i)) != null && a > b) a = b; } return a; }; d3.max = function(array, f) { var i = -1, n = array.length, a, b; if (arguments.length === 1) { while (++i < n) if ((b = array[i]) != null && b >= b) { a = b; break; } while (++i < n) if ((b = array[i]) != null && b > a) a = b; } else { while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) { a = b; break; } while (++i < n) if ((b = f.call(array, array[i], i)) != null && b > a) a = b; } return a; }; d3.extent = function(array, f) { var i = -1, n = array.length, a, b, c; if (arguments.length === 1) { while (++i < n) if ((b = array[i]) != null && b >= b) { a = c = b; break; } while (++i < n) if ((b = array[i]) != null) { if (a > b) a = b; if (c < b) c = b; } } else { while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) { a = c = b; break; } while (++i < n) if ((b = f.call(array, array[i], i)) != null) { if (a > b) a = b; if (c < b) c = b; } } return [ a, c ]; }; function d3_number(x) { return x === null ? NaN : +x; } function d3_numeric(x) { return !isNaN(x); } d3.sum = function(array, f) { var s = 0, n = array.length, a, i = -1; if (arguments.length === 1) { while (++i < n) if (d3_numeric(a = +array[i])) s += a; } else { while (++i < n) if (d3_numeric(a = +f.call(array, array[i], i))) s += a; } return s; }; d3.mean = function(array, f) { var s = 0, n = array.length, a, i = -1, j = n; if (arguments.length === 1) { while (++i < n) if (d3_numeric(a = d3_number(array[i]))) s += a; else --j; } else { while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) s += a; else --j; } if (j) return s / j; }; d3.quantile = function(values, p) { var H = (values.length - 1) * p + 1, h = Math.floor(H), v = +values[h - 1], e = H - h; return e ? v + e * (values[h] - v) : v; }; d3.median = function(array, f) { var numbers = [], n = array.length, a, i = -1; if (arguments.length === 1) { while (++i < n) if (d3_numeric(a = d3_number(array[i]))) numbers.push(a); } else { while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) numbers.push(a); } if (numbers.length) return d3.quantile(numbers.sort(d3_ascending), .5); }; d3.variance = function(array, f) { var n = array.length, m = 0, a, d, s = 0, i = -1, j = 0; if (arguments.length === 1) { while (++i < n) { if (d3_numeric(a = d3_number(array[i]))) { d = a - m; m += d / ++j; s += d * (a - m); } } } else { while (++i < n) { if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) { d = a - m; m += d / ++j; s += d * (a - m); } } } if (j > 1) return s / (j - 1); }; d3.deviation = function() { var v = d3.variance.apply(this, arguments); return v ? Math.sqrt(v) : v; }; function d3_bisector(compare) { return { left: function(a, x, lo, hi) { if (arguments.length < 3) lo = 0; if (arguments.length < 4) hi = a.length; while (lo < hi) { var mid = lo + hi >>> 1; if (compare(a[mid], x) < 0) lo = mid + 1; else hi = mid; } return lo; }, right: function(a, x, lo, hi) { if (arguments.length < 3) lo = 0; if (arguments.length < 4) hi = a.length; while (lo < hi) { var mid = lo + hi >>> 1; if (compare(a[mid], x) > 0) hi = mid; else lo = mid + 1; } return lo; } }; } var d3_bisect = d3_bisector(d3_ascending); d3.bisectLeft = d3_bisect.left; d3.bisect = d3.bisectRight = d3_bisect.right; d3.bisector = function(f) { return d3_bisector(f.length === 1 ? function(d, x) { return d3_ascending(f(d), x); } : f); }; d3.shuffle = function(array, i0, i1) { if ((m = arguments.length) < 3) { i1 = array.length; if (m < 2) i0 = 0; } var m = i1 - i0, t, i; while (m) { i = Math.random() * m-- | 0; t = array[m + i0], array[m + i0] = array[i + i0], array[i + i0] = t; } return array; }; d3.permute = function(array, indexes) { var i = indexes.length, permutes = new Array(i); while (i--) permutes[i] = array[indexes[i]]; return permutes; }; d3.pairs = function(array) { var i = 0, n = array.length - 1, p0, p1 = array[0], pairs = new Array(n < 0 ? 0 : n); while (i < n) pairs[i] = [ p0 = p1, p1 = array[++i] ]; return pairs; }; d3.transpose = function(matrix) { if (!(n = matrix.length)) return []; for (var i = -1, m = d3.min(matrix, d3_transposeLength), transpose = new Array(m); ++i < m; ) { for (var j = -1, n, row = transpose[i] = new Array(n); ++j < n; ) { row[j] = matrix[j][i]; } } return transpose; }; function d3_transposeLength(d) { return d.length; } d3.zip = function() { return d3.transpose(arguments); }; d3.keys = function(map) { var keys = []; for (var key in map) keys.push(key); return keys; }; d3.values = function(map) { var values = []; for (var key in map) values.push(map[key]); return values; }; d3.entries = function(map) { var entries = []; for (var key in map) entries.push({ key: key, value: map[key] }); return entries; }; d3.merge = function(arrays) { var n = arrays.length, m, i = -1, j = 0, merged, array; while (++i < n) j += arrays[i].length; merged = new Array(j); while (--n >= 0) { array = arrays[n]; m = array.length; while (--m >= 0) { merged[--j] = array[m]; } } return merged; }; var abs = Math.abs; d3.range = function(start, stop, step) { if (arguments.length < 3) { step = 1; if (arguments.length < 2) { stop = start; start = 0; } } if ((stop - start) / step === Infinity) throw new Error("infinite range"); var range = [], k = d3_range_integerScale(abs(step)), i = -1, j; start *= k, stop *= k, step *= k; if (step < 0) while ((j = start + step * ++i) > stop) range.push(j / k); else while ((j = start + step * ++i) < stop) range.push(j / k); return range; }; function d3_range_integerScale(x) { var k = 1; while (x * k % 1) k *= 10; return k; } function d3_class(ctor, properties) { for (var key in properties) { Object.defineProperty(ctor.prototype, key, { value: properties[key], enumerable: false }); } } d3.map = function(object, f) { var map = new d3_Map(); if (object instanceof d3_Map) { object.forEach(function(key, value) { map.set(key, value); }); } else if (Array.isArray(object)) { var i = -1, n = object.length, o; if (arguments.length === 1) while (++i < n) map.set(i, object[i]); else while (++i < n) map.set(f.call(object, o = object[i], i), o); } else { for (var key in object) map.set(key, object[key]); } return map; }; function d3_Map() { this._ = Object.create(null); } var d3_map_proto = "__proto__", d3_map_zero = "\x00"; d3_class(d3_Map, { has: d3_map_has, get: function(key) { return this._[d3_map_escape(key)]; }, set: function(key, value) { return this._[d3_map_escape(key)] = value; }, remove: d3_map_remove, keys: d3_map_keys, values: function() { var values = []; for (var key in this._) values.push(this._[key]); return values; }, entries: function() { var entries = []; for (var key in this._) entries.push({ key: d3_map_unescape(key), value: this._[key] }); return entries; }, size: d3_map_size, empty: d3_map_empty, forEach: function(f) { for (var key in this._) f.call(this, d3_map_unescape(key), this._[key]); } }); function d3_map_escape(key) { return (key += "") === d3_map_proto || key[0] === d3_map_zero ? d3_map_zero + key : key; } function d3_map_unescape(key) { return (key += "")[0] === d3_map_zero ? key.slice(1) : key; } function d3_map_has(key) { return d3_map_escape(key) in this._; } function d3_map_remove(key) { return (key = d3_map_escape(key)) in this._ && delete this._[key]; } function d3_map_keys() { var keys = []; for (var key in this._) keys.push(d3_map_unescape(key)); return keys; } function d3_map_size() { var size = 0; for (var key in this._) ++size; return size; } function d3_map_empty() { for (var key in this._) return false; return true; } d3.nest = function() { var nest = {}, keys = [], sortKeys = [], sortValues, rollup; function map(mapType, array, depth) { if (depth >= keys.length) return rollup ? rollup.call(nest, array) : sortValues ? array.sort(sortValues) : array; var i = -1, n = array.length, key = keys[depth++], keyValue, object, setter, valuesByKey = new d3_Map(), values; while (++i < n) { if (values = valuesByKey.get(keyValue = key(object = array[i]))) { values.push(object); } else { valuesByKey.set(keyValue, [ object ]); } } if (mapType) { object = mapType(); setter = function(keyValue, values) { object.set(keyValue, map(mapType, values, depth)); }; } else { object = {}; setter = function(keyValue, values) { object[keyValue] = map(mapType, values, depth); }; } valuesByKey.forEach(setter); return object; } function entries(map, depth) { if (depth >= keys.length) return map; var array = [], sortKey = sortKeys[depth++]; map.forEach(function(key, keyMap) { array.push({ key: key, values: entries(keyMap, depth) }); }); return sortKey ? array.sort(function(a, b) { return sortKey(a.key, b.key); }) : array; } nest.map = function(array, mapType) { return map(mapType, array, 0); }; nest.entries = function(array) { return entries(map(d3.map, array, 0), 0); }; nest.key = function(d) { keys.push(d); return nest; }; nest.sortKeys = function(order) { sortKeys[keys.length - 1] = order; return nest; }; nest.sortValues = function(order) { sortValues = order; return nest; }; nest.rollup = function(f) { rollup = f; return nest; }; return nest; }; d3.set = function(array) { var set = new d3_Set(); if (array) for (var i = 0, n = array.length; i < n; ++i) set.add(array[i]); return set; }; function d3_Set() { this._ = Object.create(null); } d3_class(d3_Set, { has: d3_map_has, add: function(key) { this._[d3_map_escape(key += "")] = true; return key; }, remove: d3_map_remove, values: d3_map_keys, size: d3_map_size, empty: d3_map_empty, forEach: function(f) { for (var key in this._) f.call(this, d3_map_unescape(key)); } }); d3.behavior = {}; function d3_identity(d) { return d; } d3.rebind = function(target, source) { var i = 1, n = arguments.length, method; while (++i < n) target[method = arguments[i]] = d3_rebind(target, source, source[method]); return target; }; function d3_rebind(target, source, method) { return function() { var value = method.apply(source, arguments); return value === source ? target : value; }; } function d3_vendorSymbol(object, name) { if (name in object) return name; name = name.charAt(0).toUpperCase() + name.slice(1); for (var i = 0, n = d3_vendorPrefixes.length; i < n; ++i) { var prefixName = d3_vendorPrefixes[i] + name; if (prefixName in object) return prefixName; } } var d3_vendorPrefixes = [ "webkit", "ms", "moz", "Moz", "o", "O" ]; function d3_noop() {} d3.dispatch = function() { var dispatch = new d3_dispatch(), i = -1, n = arguments.length; while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch); return dispatch; }; function d3_dispatch() {} d3_dispatch.prototype.on = function(type, listener) { var i = type.indexOf("."), name = ""; if (i >= 0) { name = type.slice(i + 1); type = type.slice(0, i); } if (type) return arguments.length < 2 ? this[type].on(name) : this[type].on(name, listener); if (arguments.length === 2) { if (listener == null) for (type in this) { if (this.hasOwnProperty(type)) this[type].on(name, null); } return this; } }; function d3_dispatch_event(dispatch) { var listeners = [], listenerByName = new d3_Map(); function event() { var z = listeners, i = -1, n = z.length, l; while (++i < n) if (l = z[i].on) l.apply(this, arguments); return dispatch; } event.on = function(name, listener) { var l = listenerByName.get(name), i; if (arguments.length < 2) return l && l.on; if (l) { l.on = null; listeners = listeners.slice(0, i = listeners.indexOf(l)).concat(listeners.slice(i + 1)); listenerByName.remove(name); } if (listener) listeners.push(listenerByName.set(name, { on: listener })); return dispatch; }; return event; } d3.event = null; function d3_eventPreventDefault() { d3.event.preventDefault(); } function d3_eventSource() { var e = d3.event, s; while (s = e.sourceEvent) e = s; return e; } function d3_eventDispatch(target) { var dispatch = new d3_dispatch(), i = 0, n = arguments.length; while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch); dispatch.of = function(thiz, argumentz) { return function(e1) { try { var e0 = e1.sourceEvent = d3.event; e1.target = target; d3.event = e1; dispatch[e1.type].apply(thiz, argumentz); } finally { d3.event = e0; } }; }; return dispatch; } d3.requote = function(s) { return s.replace(d3_requote_re, "\\$&"); }; var d3_requote_re = /[\\\^\$\*\+\?\|\[\]\(\)\.\{\}]/g; var d3_subclass = {}.__proto__ ? function(object, prototype) { object.__proto__ = prototype; } : function(object, prototype) { for (var property in prototype) object[property] = prototype[property]; }; function d3_selection(groups) { d3_subclass(groups, d3_selectionPrototype); return groups; } var d3_select = function(s, n) { return n.querySelector(s); }, d3_selectAll = function(s, n) { return n.querySelectorAll(s); }, d3_selectMatches = function(n, s) { var d3_selectMatcher = n.matches || n[d3_vendorSymbol(n, "matchesSelector")]; d3_selectMatches = function(n, s) { return d3_selectMatcher.call(n, s); }; return d3_selectMatches(n, s); }; if (typeof Sizzle === "function") { d3_select = function(s, n) { return Sizzle(s, n)[0] || null; }; d3_selectAll = Sizzle; d3_selectMatches = Sizzle.matchesSelector; } d3.selection = function() { return d3.select(d3_document.documentElement); }; var d3_selectionPrototype = d3.selection.prototype = []; d3_selectionPrototype.select = function(selector) { var subgroups = [], subgroup, subnode, group, node; selector = d3_selection_selector(selector); for (var j = -1, m = this.length; ++j < m; ) { subgroups.push(subgroup = []); subgroup.parentNode = (group = this[j]).parentNode; for (var i = -1, n = group.length; ++i < n; ) { if (node = group[i]) { subgroup.push(subnode = selector.call(node, node.__data__, i, j)); if (subnode && "__data__" in node) subnode.__data__ = node.__data__; } else { subgroup.push(null); } } } return d3_selection(subgroups); }; function d3_selection_selector(selector) { return typeof selector === "function" ? selector : function() { return d3_select(selector, this); }; } d3_selectionPrototype.selectAll = function(selector) { var subgroups = [], subgroup, node; selector = d3_selection_selectorAll(selector); for (var j = -1, m = this.length; ++j < m; ) { for (var group = this[j], i = -1, n = group.length; ++i < n; ) { if (node = group[i]) { subgroups.push(subgroup = d3_array(selector.call(node, node.__data__, i, j))); subgroup.parentNode = node; } } } return d3_selection(subgroups); }; function d3_selection_selectorAll(selector) { return typeof selector === "function" ? selector : function() { return d3_selectAll(selector, this); }; } var d3_nsXhtml = "http://www.w3.org/1999/xhtml"; var d3_nsPrefix = { svg: "http://www.w3.org/2000/svg", xhtml: d3_nsXhtml, xlink: "http://www.w3.org/1999/xlink", xml: "http://www.w3.org/XML/1998/namespace", xmlns: "http://www.w3.org/2000/xmlns/" }; d3.ns = { prefix: d3_nsPrefix, qualify: function(name) { var i = name.indexOf(":"), prefix = name; if (i >= 0 && (prefix = name.slice(0, i)) !== "xmlns") name = name.slice(i + 1); return d3_nsPrefix.hasOwnProperty(prefix) ? { space: d3_nsPrefix[prefix], local: name } : name; } }; d3_selectionPrototype.attr = function(name, value) { if (arguments.length < 2) { if (typeof name === "string") { var node = this.node(); name = d3.ns.qualify(name); return name.local ? node.getAttributeNS(name.space, name.local) : node.getAttribute(name); } for (value in name) this.each(d3_selection_attr(value, name[value])); return this; } return this.each(d3_selection_attr(name, value)); }; function d3_selection_attr(name, value) { name = d3.ns.qualify(name); function attrNull() { this.removeAttribute(name); } function attrNullNS() { this.removeAttributeNS(name.space, name.local); } function attrConstant() { this.setAttribute(name, value); } function attrConstantNS() { this.setAttributeNS(name.space, name.local, value); } function attrFunction() { var x = value.apply(this, arguments); if (x == null) this.removeAttribute(name); else this.setAttribute(name, x); } function attrFunctionNS() { var x = value.apply(this, arguments); if (x == null) this.removeAttributeNS(name.space, name.local); else this.setAttributeNS(name.space, name.local, x); } return value == null ? name.local ? attrNullNS : attrNull : typeof value === "function" ? name.local ? attrFunctionNS : attrFunction : name.local ? attrConstantNS : attrConstant; } function d3_collapse(s) { return s.trim().replace(/\s+/g, " "); } d3_selectionPrototype.classed = function(name, value) { if (arguments.length < 2) { if (typeof name === "string") { var node = this.node(), n = (name = d3_selection_classes(name)).length, i = -1; if (value = node.classList) { while (++i < n) if (!value.contains(name[i])) return false; } else { value = node.getAttribute("class"); while (++i < n) if (!d3_selection_classedRe(name[i]).test(value)) return false; } return true; } for (value in name) this.each(d3_selection_classed(value, name[value])); return this; } return this.each(d3_selection_classed(name, value)); }; function d3_selection_classedRe(name) { return new RegExp("(?:^|\\s+)" + d3.requote(name) + "(?:\\s+|$)", "g"); } function d3_selection_classes(name) { return (name + "").trim().split(/^|\s+/); } function d3_selection_classed(name, value) { name = d3_selection_classes(name).map(d3_selection_classedName); var n = name.length; function classedConstant() { var i = -1; while (++i < n) name[i](this, value); } function classedFunction() { var i = -1, x = value.apply(this, arguments); while (++i < n) name[i](this, x); } return typeof value === "function" ? classedFunction : classedConstant; } function d3_selection_classedName(name) { var re = d3_selection_classedRe(name); return function(node, value) { if (c = node.classList) return value ? c.add(name) : c.remove(name); var c = node.getAttribute("class") || ""; if (value) { re.lastIndex = 0; if (!re.test(c)) node.setAttribute("class", d3_collapse(c + " " + name)); } else { node.setAttribute("class", d3_collapse(c.replace(re, " "))); } }; } d3_selectionPrototype.style = function(name, value, priority) { var n = arguments.length; if (n < 3) { if (typeof name !== "string") { if (n < 2) value = ""; for (priority in name) this.each(d3_selection_style(priority, name[priority], value)); return this; } if (n < 2) { var node = this.node(); return d3_window(node).getComputedStyle(node, null).getPropertyValue(name); } priority = ""; } return this.each(d3_selection_style(name, value, priority)); }; function d3_selection_style(name, value, priority) { function styleNull() { this.style.removeProperty(name); } function styleConstant() { this.style.setProperty(name, value, priority); } function styleFunction() { var x = value.apply(this, arguments); if (x == null) this.style.removeProperty(name); else this.style.setProperty(name, x, priority); } return value == null ? styleNull : typeof value === "function" ? styleFunction : styleConstant; } d3_selectionPrototype.property = function(name, value) { if (arguments.length < 2) { if (typeof name === "string") return this.node()[name]; for (value in name) this.each(d3_selection_property(value, name[value])); return this; } return this.each(d3_selection_property(name, value)); }; function d3_selection_property(name, value) { function propertyNull() { delete this[name]; } function propertyConstant() { this[name] = value; } function propertyFunction() { var x = value.apply(this, arguments); if (x == null) delete this[name]; else this[name] = x; } return value == null ? propertyNull : typeof value === "function" ? propertyFunction : propertyConstant; } d3_selectionPrototype.text = function(value) { return arguments.length ? this.each(typeof value === "function" ? function() { var v = value.apply(this, arguments); this.textContent = v == null ? "" : v; } : value == null ? function() { this.textContent = ""; } : function() { this.textContent = value; }) : this.node().textContent; }; d3_selectionPrototype.html = function(value) { return arguments.length ? this.each(typeof value === "function" ? function() { var v = value.apply(this, arguments); this.innerHTML = v == null ? "" : v; } : value == null ? function() { this.innerHTML = ""; } : function() { this.innerHTML = value; }) : this.node().innerHTML; }; d3_selectionPrototype.append = function(name) { name = d3_selection_creator(name); return this.select(function() { return this.appendChild(name.apply(this, arguments)); }); }; function d3_selection_creator(name) { function create() { var document = this.ownerDocument, namespace = this.namespaceURI; return namespace === d3_nsXhtml && document.documentElement.namespaceURI === d3_nsXhtml ? document.createElement(name) : document.createElementNS(namespace, name); } function createNS() { return this.ownerDocument.createElementNS(name.space, name.local); } return typeof name === "function" ? name : (name = d3.ns.qualify(name)).local ? createNS : create; } d3_selectionPrototype.insert = function(name, before) { name = d3_selection_creator(name); before = d3_selection_selector(before); return this.select(function() { return this.insertBefore(name.apply(this, arguments), before.apply(this, arguments) || null); }); }; d3_selectionPrototype.remove = function() { return this.each(d3_selectionRemove); }; function d3_selectionRemove() { var parent = this.parentNode; if (parent) parent.removeChild(this); } d3_selectionPrototype.data = function(value, key) { var i = -1, n = this.length, group, node; if (!arguments.length) { value = new Array(n = (group = this[0]).length); while (++i < n) { if (node = group[i]) { value[i] = node.__data__; } } return value; } function bind(group, groupData) { var i, n = group.length, m = groupData.length, n0 = Math.min(n, m), updateNodes = new Array(m), enterNodes = new Array(m), exitNodes = new Array(n), node, nodeData; if (key) { var nodeByKeyValue = new d3_Map(), keyValues = new Array(n), keyValue; for (i = -1; ++i < n; ) { if (node = group[i]) { if (nodeByKeyValue.has(keyValue = key.call(node, node.__data__, i))) { exitNodes[i] = node; } else { nodeByKeyValue.set(keyValue, node); } keyValues[i] = keyValue; } } for (i = -1; ++i < m; ) { if (!(node = nodeByKeyValue.get(keyValue = key.call(groupData, nodeData = groupData[i], i)))) { enterNodes[i] = d3_selection_dataNode(nodeData); } else if (node !== true) { updateNodes[i] = node; node.__data__ = nodeData; } nodeByKeyValue.set(keyValue, true); } for (i = -1; ++i < n; ) { if (i in keyValues && nodeByKeyValue.get(keyValues[i]) !== true) { exitNodes[i] = group[i]; } } } else { for (i = -1; ++i < n0; ) { node = group[i]; nodeData = groupData[i]; if (node) { node.__data__ = nodeData; updateNodes[i] = node; } else { enterNodes[i] = d3_selection_dataNode(nodeData); } } for (;i < m; ++i) { enterNodes[i] = d3_selection_dataNode(groupData[i]); } for (;i < n; ++i) { exitNodes[i] = group[i]; } } enterNodes.update = updateNodes; enterNodes.parentNode = updateNodes.parentNode = exitNodes.parentNode = group.parentNode; enter.push(enterNodes); update.push(updateNodes); exit.push(exitNodes); } var enter = d3_selection_enter([]), update = d3_selection([]), exit = d3_selection([]); if (typeof value === "function") { while (++i < n) { bind(group = this[i], value.call(group, group.parentNode.__data__, i)); } } else { while (++i < n) { bind(group = this[i], value); } } update.enter = function() { return enter; }; update.exit = function() { return exit; }; return update; }; function d3_selection_dataNode(data) { return { __data__: data }; } d3_selectionPrototype.datum = function(value) { return arguments.length ? this.property("__data__", value) : this.property("__data__"); }; d3_selectionPrototype.filter = function(filter) { var subgroups = [], subgroup, group, node; if (typeof filter !== "function") filter = d3_selection_filter(filter); for (var j = 0, m = this.length; j < m; j++) { subgroups.push(subgroup = []); subgroup.parentNode = (group = this[j]).parentNode; for (var i = 0, n = group.length; i < n; i++) { if ((node = group[i]) && filter.call(node, node.__data__, i, j)) { subgroup.push(node); } } } return d3_selection(subgroups); }; function d3_selection_filter(selector) { return function() { return d3_selectMatches(this, selector); }; } d3_selectionPrototype.order = function() { for (var j = -1, m = this.length; ++j < m; ) { for (var group = this[j], i = group.length - 1, next = group[i], node; --i >= 0; ) { if (node = group[i]) { if (next && next !== node.nextSibling) next.parentNode.insertBefore(node, next); next = node; } } } return this; }; d3_selectionPrototype.sort = function(comparator) { comparator = d3_selection_sortComparator.apply(this, arguments); for (var j = -1, m = this.length; ++j < m; ) this[j].sort(comparator); return this.order(); }; function d3_selection_sortComparator(comparator) { if (!arguments.length) comparator = d3_ascending; return function(a, b) { return a && b ? comparator(a.__data__, b.__data__) : !a - !b; }; } d3_selectionPrototype.each = function(callback) { return d3_selection_each(this, function(node, i, j) { callback.call(node, node.__data__, i, j); }); }; function d3_selection_each(groups, callback) { for (var j = 0, m = groups.length; j < m; j++) { for (var group = groups[j], i = 0, n = group.length, node; i < n; i++) { if (node = group[i]) callback(node, i, j); } } return groups; } d3_selectionPrototype.call = function(callback) { var args = d3_array(arguments); callback.apply(args[0] = this, args); return this; }; d3_selectionPrototype.empty = function() { return !this.node(); }; d3_selectionPrototype.node = function() { for (var j = 0, m = this.length; j < m; j++) { for (var group = this[j], i = 0, n = group.length; i < n; i++) { var node = group[i]; if (node) return node; } } return null; }; d3_selectionPrototype.size = function() { var n = 0; d3_selection_each(this, function() { ++n; }); return n; }; function d3_selection_enter(selection) { d3_subclass(selection, d3_selection_enterPrototype); return selection; } var d3_selection_enterPrototype = []; d3.selection.enter = d3_selection_enter; d3.selection.enter.prototype = d3_selection_enterPrototype; d3_selection_enterPrototype.append = d3_selectionPrototype.append; d3_selection_enterPrototype.empty = d3_selectionPrototype.empty; d3_selection_enterPrototype.node = d3_selectionPrototype.node; d3_selection_enterPrototype.call = d3_selectionPrototype.call; d3_selection_enterPrototype.size = d3_selectionPrototype.size; d3_selection_enterPrototype.select = function(selector) { var subgroups = [], subgroup, subnode, upgroup, group, node; for (var j = -1, m = this.length; ++j < m; ) { upgroup = (group = this[j]).update; subgroups.push(subgroup = []); subgroup.parentNode = group.parentNode; for (var i = -1, n = group.length; ++i < n; ) { if (node = group[i]) { subgroup.push(upgroup[i] = subnode = selector.call(group.parentNode, node.__data__, i, j)); subnode.__data__ = node.__data__; } else { subgroup.push(null); } } } return d3_selection(subgroups); }; d3_selection_enterPrototype.insert = function(name, before) { if (arguments.length < 2) before = d3_selection_enterInsertBefore(this); return d3_selectionPrototype.insert.call(this, name, before); }; function d3_selection_enterInsertBefore(enter) { var i0, j0; return function(d, i, j) { var group = enter[j].update, n = group.length, node; if (j != j0) j0 = j, i0 = 0; if (i >= i0) i0 = i + 1; while (!(node = group[i0]) && ++i0 < n) ; return node; }; } d3.select = function(node) { var group; if (typeof node === "string") { group = [ d3_select(node, d3_document) ]; group.parentNode = d3_document.documentElement; } else { group = [ node ]; group.parentNode = d3_documentElement(node); } return d3_selection([ group ]); }; d3.selectAll = function(nodes) { var group; if (typeof nodes === "string") { group = d3_array(d3_selectAll(nodes, d3_document)); group.parentNode = d3_document.documentElement; } else { group = d3_array(nodes); group.parentNode = null; } return d3_selection([ group ]); }; d3_selectionPrototype.on = function(type, listener, capture) { var n = arguments.length; if (n < 3) { if (typeof type !== "string") { if (n < 2) listener = false; for (capture in type) this.each(d3_selection_on(capture, type[capture], listener)); return this; } if (n < 2) return (n = this.node()["__on" + type]) && n._; capture = false; } return this.each(d3_selection_on(type, listener, capture)); }; function d3_selection_on(type, listener, capture) { var name = "__on" + type, i = type.indexOf("."), wrap = d3_selection_onListener; if (i > 0) type = type.slice(0, i); var filter = d3_selection_onFilters.get(type); if (filter) type = filter, wrap = d3_selection_onFilter; function onRemove() { var l = this[name]; if (l) { this.removeEventListener(type, l, l.$); delete this[name]; } } function onAdd() { var l = wrap(listener, d3_array(arguments)); onRemove.call(this); this.addEventListener(type, this[name] = l, l.$ = capture); l._ = listener; } function removeAll() { var re = new RegExp("^__on([^.]+)" + d3.requote(type) + "$"), match; for (var name in this) { if (match = name.match(re)) { var l = this[name]; this.removeEventListener(match[1], l, l.$); delete this[name]; } } } return i ? listener ? onAdd : onRemove : listener ? d3_noop : removeAll; } var d3_selection_onFilters = d3.map({ mouseenter: "mouseover", mouseleave: "mouseout" }); if (d3_document) { d3_selection_onFilters.forEach(function(k) { if ("on" + k in d3_document) d3_selection_onFilters.remove(k); }); } function d3_selection_onListener(listener, argumentz) { return function(e) { var o = d3.event; d3.event = e; argumentz[0] = this.__data__; try { listener.apply(this, argumentz); } finally { d3.event = o; } }; } function d3_selection_onFilter(listener, argumentz) { var l = d3_selection_onListener(listener, argumentz); return function(e) { var target = this, related = e.relatedTarget; if (!related || related !== target && !(related.compareDocumentPosition(target) & 8)) { l.call(target, e); } }; } var d3_event_dragSelect, d3_event_dragId = 0; function d3_event_dragSuppress(node) { var name = ".dragsuppress-" + ++d3_event_dragId, click = "click" + name, w = d3.select(d3_window(node)).on("touchmove" + name, d3_eventPreventDefault).on("dragstart" + name, d3_eventPreventDefault).on("selectstart" + name, d3_eventPreventDefault); if (d3_event_dragSelect == null) { d3_event_dragSelect = "onselectstart" in node ? false : d3_vendorSymbol(node.style, "userSelect"); } if (d3_event_dragSelect) { var style = d3_documentElement(node).style, select = style[d3_event_dragSelect]; style[d3_event_dragSelect] = "none"; } return function(suppressClick) { w.on(name, null); if (d3_event_dragSelect) style[d3_event_dragSelect] = select; if (suppressClick) { var off = function() { w.on(click, null); }; w.on(click, function() { d3_eventPreventDefault(); off(); }, true); setTimeout(off, 0); } }; } d3.mouse = function(container) { return d3_mousePoint(container, d3_eventSource()); }; var d3_mouse_bug44083 = this.navigator && /WebKit/.test(this.navigator.userAgent) ? -1 : 0; function d3_mousePoint(container, e) { if (e.changedTouches) e = e.changedTouches[0]; var svg = container.ownerSVGElement || container; if (svg.createSVGPoint) { var point = svg.createSVGPoint(); if (d3_mouse_bug44083 < 0) { var window = d3_window(container); if (window.scrollX || window.scrollY) { svg = d3.select("body").append("svg").style({ position: "absolute", top: 0, left: 0, margin: 0, padding: 0, border: "none" }, "important"); var ctm = svg[0][0].getScreenCTM(); d3_mouse_bug44083 = !(ctm.f || ctm.e); svg.remove(); } } if (d3_mouse_bug44083) point.x = e.pageX, point.y = e.pageY; else point.x = e.clientX, point.y = e.clientY; point = point.matrixTransform(container.getScreenCTM().inverse()); return [ point.x, point.y ]; } var rect = container.getBoundingClientRect(); return [ e.clientX - rect.left - container.clientLeft, e.clientY - rect.top - container.clientTop ]; } d3.touch = function(container, touches, identifier) { if (arguments.length < 3) identifier = touches, touches = d3_eventSource().changedTouches; if (touches) for (var i = 0, n = touches.length, touch; i < n; ++i) { if ((touch = touches[i]).identifier === identifier) { return d3_mousePoint(container, touch); } } }; d3.behavior.drag = function() { var event = d3_eventDispatch(drag, "drag", "dragstart", "dragend"), origin = null, mousedown = dragstart(d3_noop, d3.mouse, d3_window, "mousemove", "mouseup"), touchstart = dragstart(d3_behavior_dragTouchId, d3.touch, d3_identity, "touchmove", "touchend"); function drag() { this.on("mousedown.drag", mousedown).on("touchstart.drag", touchstart); } function dragstart(id, position, subject, move, end) { return function() { var that = this, target = d3.event.target.correspondingElement || d3.event.target, parent = that.parentNode, dispatch = event.of(that, arguments), dragged = 0, dragId = id(), dragName = ".drag" + (dragId == null ? "" : "-" + dragId), dragOffset, dragSubject = d3.select(subject(target)).on(move + dragName, moved).on(end + dragName, ended), dragRestore = d3_event_dragSuppress(target), position0 = position(parent, dragId); if (origin) { dragOffset = origin.apply(that, arguments); dragOffset = [ dragOffset.x - position0[0], dragOffset.y - position0[1] ]; } else { dragOffset = [ 0, 0 ]; } dispatch({ type: "dragstart" }); function moved() { var position1 = position(parent, dragId), dx, dy; if (!position1) return; dx = position1[0] - position0[0]; dy = position1[1] - position0[1]; dragged |= dx | dy; position0 = position1; dispatch({ type: "drag", x: position1[0] + dragOffset[0], y: position1[1] + dragOffset[1], dx: dx, dy: dy }); } function ended() { if (!position(parent, dragId)) return; dragSubject.on(move + dragName, null).on(end + dragName, null); dragRestore(dragged); dispatch({ type: "dragend" }); } }; } drag.origin = function(x) { if (!arguments.length) return origin; origin = x; return drag; }; return d3.rebind(drag, event, "on"); }; function d3_behavior_dragTouchId() { return d3.event.changedTouches[0].identifier; } d3.touches = function(container, touches) { if (arguments.length < 2) touches = d3_eventSource().touches; return touches ? d3_array(touches).map(function(touch) { var point = d3_mousePoint(container, touch); point.identifier = touch.identifier; return point; }) : []; }; var ε = 1e-6, ε2 = ε * ε, π = Math.PI, τ = 2 * π, τε = τ - ε, halfπ = π / 2, d3_radians = π / 180, d3_degrees = 180 / π; function d3_sgn(x) { return x > 0 ? 1 : x < 0 ? -1 : 0; } function d3_cross2d(a, b, c) { return (b[0] - a[0]) * (c[1] - a[1]) - (b[1] - a[1]) * (c[0] - a[0]); } function d3_acos(x) { return x > 1 ? 0 : x < -1 ? π : Math.acos(x); } function d3_asin(x) { return x > 1 ? halfπ : x < -1 ? -halfπ : Math.asin(x); } function d3_sinh(x) { return ((x = Math.exp(x)) - 1 / x) / 2; } function d3_cosh(x) { return ((x = Math.exp(x)) + 1 / x) / 2; } function d3_tanh(x) { return ((x = Math.exp(2 * x)) - 1) / (x + 1); } function d3_haversin(x) { return (x = Math.sin(x / 2)) * x; } var ρ = Math.SQRT2, ρ2 = 2, ρ4 = 4; d3.interpolateZoom = function(p0, p1) { var ux0 = p0[0], uy0 = p0[1], w0 = p0[2], ux1 = p1[0], uy1 = p1[1], w1 = p1[2], dx = ux1 - ux0, dy = uy1 - uy0, d2 = dx * dx + dy * dy, i, S; if (d2 < ε2) { S = Math.log(w1 / w0) / ρ; i = function(t) { return [ ux0 + t * dx, uy0 + t * dy, w0 * Math.exp(ρ * t * S) ]; }; } else { var d1 = Math.sqrt(d2), b0 = (w1 * w1 - w0 * w0 + ρ4 * d2) / (2 * w0 * ρ2 * d1), b1 = (w1 * w1 - w0 * w0 - ρ4 * d2) / (2 * w1 * ρ2 * d1), r0 = Math.log(Math.sqrt(b0 * b0 + 1) - b0), r1 = Math.log(Math.sqrt(b1 * b1 + 1) - b1); S = (r1 - r0) / ρ; i = function(t) { var s = t * S, coshr0 = d3_cosh(r0), u = w0 / (ρ2 * d1) * (coshr0 * d3_tanh(ρ * s + r0) - d3_sinh(r0)); return [ ux0 + u * dx, uy0 + u * dy, w0 * coshr0 / d3_cosh(ρ * s + r0) ]; }; } i.duration = S * 1e3; return i; }; d3.behavior.zoom = function() { var view = { x: 0, y: 0, k: 1 }, translate0, center0, center, size = [ 960, 500 ], scaleExtent = d3_behavior_zoomInfinity, duration = 250, zooming = 0, mousedown = "mousedown.zoom", mousemove = "mousemove.zoom", mouseup = "mouseup.zoom", mousewheelTimer, touchstart = "touchstart.zoom", touchtime, event = d3_eventDispatch(zoom, "zoomstart", "zoom", "zoomend"), x0, x1, y0, y1; if (!d3_behavior_zoomWheel) { d3_behavior_zoomWheel = "onwheel" in d3_document ? (d3_behavior_zoomDelta = function() { return -d3.event.deltaY * (d3.event.deltaMode ? 120 : 1); }, "wheel") : "onmousewheel" in d3_document ? (d3_behavior_zoomDelta = function() { return d3.event.wheelDelta; }, "mousewheel") : (d3_behavior_zoomDelta = function() { return -d3.event.detail; }, "MozMousePixelScroll"); } function zoom(g) { g.on(mousedown, mousedowned).on(d3_behavior_zoomWheel + ".zoom", mousewheeled).on("dblclick.zoom", dblclicked).on(touchstart, touchstarted); } zoom.event = function(g) { g.each(function() { var dispatch = event.of(this, arguments), view1 = view; if (d3_transitionInheritId) { d3.select(this).transition().each("start.zoom", function() { view = this.__chart__ || { x: 0, y: 0, k: 1 }; zoomstarted(dispatch); }).tween("zoom:zoom", function() { var dx = size[0], dy = size[1], cx = center0 ? center0[0] : dx / 2, cy = center0 ? center0[1] : dy / 2, i = d3.interpolateZoom([ (cx - view.x) / view.k, (cy - view.y) / view.k, dx / view.k ], [ (cx - view1.x) / view1.k, (cy - view1.y) / view1.k, dx / view1.k ]); return function(t) { var l = i(t), k = dx / l[2]; this.__chart__ = view = { x: cx - l[0] * k, y: cy - l[1] * k, k: k }; zoomed(dispatch); }; }).each("interrupt.zoom", function() { zoomended(dispatch); }).each("end.zoom", function() { zoomended(dispatch); }); } else { this.__chart__ = view; zoomstarted(dispatch); zoomed(dispatch); zoomended(dispatch); } }); }; zoom.translate = function(_) { if (!arguments.length) return [ view.x, view.y ]; view = { x: +_[0], y: +_[1], k: view.k }; rescale(); return zoom; }; zoom.scale = function(_) { if (!arguments.length) return view.k; view = { x: view.x, y: view.y, k: null }; scaleTo(+_); rescale(); return zoom; }; zoom.scaleExtent = function(_) { if (!arguments.length) return scaleExtent; scaleExtent = _ == null ? d3_behavior_zoomInfinity : [ +_[0], +_[1] ]; return zoom; }; zoom.center = function(_) { if (!arguments.length) return center; center = _ && [ +_[0], +_[1] ]; return zoom; }; zoom.size = function(_) { if (!arguments.length) return size; size = _ && [ +_[0], +_[1] ]; return zoom; }; zoom.duration = function(_) { if (!arguments.length) return duration; duration = +_; return zoom; }; zoom.x = function(z) { if (!arguments.length) return x1; x1 = z; x0 = z.copy(); view = { x: 0, y: 0, k: 1 }; return zoom; }; zoom.y = function(z) { if (!arguments.length) return y1; y1 = z; y0 = z.copy(); view = { x: 0, y: 0, k: 1 }; return zoom; }; function location(p) { return [ (p[0] - view.x) / view.k, (p[1] - view.y) / view.k ]; } function point(l) { return [ l[0] * view.k + view.x, l[1] * view.k + view.y ]; } function scaleTo(s) { view.k = Math.max(scaleExtent[0], Math.min(scaleExtent[1], s)); } function translateTo(p, l) { l = point(l); view.x += p[0] - l[0]; view.y += p[1] - l[1]; } function zoomTo(that, p, l, k) { that.__chart__ = { x: view.x, y: view.y, k: view.k }; scaleTo(Math.pow(2, k)); translateTo(center0 = p, l); that = d3.select(that); if (duration > 0) that = that.transition().duration(duration); that.call(zoom.event); } function rescale() { if (x1) x1.domain(x0.range().map(function(x) { return (x - view.x) / view.k; }).map(x0.invert)); if (y1) y1.domain(y0.range().map(function(y) { return (y - view.y) / view.k; }).map(y0.invert)); } function zoomstarted(dispatch) { if (!zooming++) dispatch({ type: "zoomstart" }); } function zoomed(dispatch) { rescale(); dispatch({ type: "zoom", scale: view.k, translate: [ view.x, view.y ] }); } function zoomended(dispatch) { if (!--zooming) dispatch({ type: "zoomend" }), center0 = null; } function mousedowned() { var that = this, dispatch = event.of(that, arguments), dragged = 0, subject = d3.select(d3_window(that)).on(mousemove, moved).on(mouseup, ended), location0 = location(d3.mouse(that)), dragRestore = d3_event_dragSuppress(that); d3_selection_interrupt.call(that); zoomstarted(dispatch); function moved() { dragged = 1; translateTo(d3.mouse(that), location0); zoomed(dispatch); } function ended() { subject.on(mousemove, null).on(mouseup, null); dragRestore(dragged); zoomended(dispatch); } } function touchstarted() { var that = this, dispatch = event.of(that, arguments), locations0 = {}, distance0 = 0, scale0, zoomName = ".zoom-" + d3.event.changedTouches[0].identifier, touchmove = "touchmove" + zoomName, touchend = "touchend" + zoomName, targets = [], subject = d3.select(that), dragRestore = d3_event_dragSuppress(that); started(); zoomstarted(dispatch); subject.on(mousedown, null).on(touchstart, started); function relocate() { var touches = d3.touches(that); scale0 = view.k; touches.forEach(function(t) { if (t.identifier in locations0) locations0[t.identifier] = location(t); }); return touches; } function started() { var target = d3.event.target; d3.select(target).on(touchmove, moved).on(touchend, ended); targets.push(target); var changed = d3.event.changedTouches; for (var i = 0, n = changed.length; i < n; ++i) { locations0[changed[i].identifier] = null; } var touches = relocate(), now = Date.now(); if (touches.length === 1) { if (now - touchtime < 500) { var p = touches[0]; zoomTo(that, p, locations0[p.identifier], Math.floor(Math.log(view.k) / Math.LN2) + 1); d3_eventPreventDefault(); } touchtime = now; } else if (touches.length > 1) { var p = touches[0], q = touches[1], dx = p[0] - q[0], dy = p[1] - q[1]; distance0 = dx * dx + dy * dy; } } function moved() { var touches = d3.touches(that), p0, l0, p1, l1; d3_selection_interrupt.call(that); for (var i = 0, n = touches.length; i < n; ++i, l1 = null) { p1 = touches[i]; if (l1 = locations0[p1.identifier]) { if (l0) break; p0 = p1, l0 = l1; } } if (l1) { var distance1 = (distance1 = p1[0] - p0[0]) * distance1 + (distance1 = p1[1] - p0[1]) * distance1, scale1 = distance0 && Math.sqrt(distance1 / distance0); p0 = [ (p0[0] + p1[0]) / 2, (p0[1] + p1[1]) / 2 ]; l0 = [ (l0[0] + l1[0]) / 2, (l0[1] + l1[1]) / 2 ]; scaleTo(scale1 * scale0); } touchtime = null; translateTo(p0, l0); zoomed(dispatch); } function ended() { if (d3.event.touches.length) { var changed = d3.event.changedTouches; for (var i = 0, n = changed.length; i < n; ++i) { delete locations0[changed[i].identifier]; } for (var identifier in locations0) { return void relocate(); } } d3.selectAll(targets).on(zoomName, null); subject.on(mousedown, mousedowned).on(touchstart, touchstarted); dragRestore(); zoomended(dispatch); } } function mousewheeled() { var dispatch = event.of(this, arguments); if (mousewheelTimer) clearTimeout(mousewheelTimer); else d3_selection_interrupt.call(this), translate0 = location(center0 = center || d3.mouse(this)), zoomstarted(dispatch); mousewheelTimer = setTimeout(function() { mousewheelTimer = null; zoomended(dispatch); }, 50); d3_eventPreventDefault(); scaleTo(Math.pow(2, d3_behavior_zoomDelta() * .002) * view.k); translateTo(center0, translate0); zoomed(dispatch); } function dblclicked() { var p = d3.mouse(this), k = Math.log(view.k) / Math.LN2; zoomTo(this, p, location(p), d3.event.shiftKey ? Math.ceil(k) - 1 : Math.floor(k) + 1); } return d3.rebind(zoom, event, "on"); }; var d3_behavior_zoomInfinity = [ 0, Infinity ], d3_behavior_zoomDelta, d3_behavior_zoomWheel; d3.color = d3_color; function d3_color() {} d3_color.prototype.toString = function() { return this.rgb() + ""; }; d3.hsl = d3_hsl; function d3_hsl(h, s, l) { return this instanceof d3_hsl ? void (this.h = +h, this.s = +s, this.l = +l) : arguments.length < 2 ? h instanceof d3_hsl ? new d3_hsl(h.h, h.s, h.l) : d3_rgb_parse("" + h, d3_rgb_hsl, d3_hsl) : new d3_hsl(h, s, l); } var d3_hslPrototype = d3_hsl.prototype = new d3_color(); d3_hslPrototype.brighter = function(k) { k = Math.pow(.7, arguments.length ? k : 1); return new d3_hsl(this.h, this.s, this.l / k); }; d3_hslPrototype.darker = function(k) { k = Math.pow(.7, arguments.length ? k : 1); return new d3_hsl(this.h, this.s, k * this.l); }; d3_hslPrototype.rgb = function() { return d3_hsl_rgb(this.h, this.s, this.l); }; function d3_hsl_rgb(h, s, l) { var m1, m2; h = isNaN(h) ? 0 : (h %= 360) < 0 ? h + 360 : h; s = isNaN(s) ? 0 : s < 0 ? 0 : s > 1 ? 1 : s; l = l < 0 ? 0 : l > 1 ? 1 : l; m2 = l <= .5 ? l * (1 + s) : l + s - l * s; m1 = 2 * l - m2; function v(h) { if (h > 360) h -= 360; else if (h < 0) h += 360; if (h < 60) return m1 + (m2 - m1) * h / 60; if (h < 180) return m2; if (h < 240) return m1 + (m2 - m1) * (240 - h) / 60; return m1; } function vv(h) { return Math.round(v(h) * 255); } return new d3_rgb(vv(h + 120), vv(h), vv(h - 120)); } d3.hcl = d3_hcl; function d3_hcl(h, c, l) { return this instanceof d3_hcl ? void (this.h = +h, this.c = +c, this.l = +l) : arguments.length < 2 ? h instanceof d3_hcl ? new d3_hcl(h.h, h.c, h.l) : h instanceof d3_lab ? d3_lab_hcl(h.l, h.a, h.b) : d3_lab_hcl((h = d3_rgb_lab((h = d3.rgb(h)).r, h.g, h.b)).l, h.a, h.b) : new d3_hcl(h, c, l); } var d3_hclPrototype = d3_hcl.prototype = new d3_color(); d3_hclPrototype.brighter = function(k) { return new d3_hcl(this.h, this.c, Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1))); }; d3_hclPrototype.darker = function(k) { return new d3_hcl(this.h, this.c, Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1))); }; d3_hclPrototype.rgb = function() { return d3_hcl_lab(this.h, this.c, this.l).rgb(); }; function d3_hcl_lab(h, c, l) { if (isNaN(h)) h = 0; if (isNaN(c)) c = 0; return new d3_lab(l, Math.cos(h *= d3_radians) * c, Math.sin(h) * c); } d3.lab = d3_lab; function d3_lab(l, a, b) { return this instanceof d3_lab ? void (this.l = +l, this.a = +a, this.b = +b) : arguments.length < 2 ? l instanceof d3_lab ? new d3_lab(l.l, l.a, l.b) : l instanceof d3_hcl ? d3_hcl_lab(l.h, l.c, l.l) : d3_rgb_lab((l = d3_rgb(l)).r, l.g, l.b) : new d3_lab(l, a, b); } var d3_lab_K = 18; var d3_lab_X = .95047, d3_lab_Y = 1, d3_lab_Z = 1.08883; var d3_labPrototype = d3_lab.prototype = new d3_color(); d3_labPrototype.brighter = function(k) { return new d3_lab(Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1)), this.a, this.b); }; d3_labPrototype.darker = function(k) { return new d3_lab(Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1)), this.a, this.b); }; d3_labPrototype.rgb = function() { return d3_lab_rgb(this.l, this.a, this.b); }; function d3_lab_rgb(l, a, b) { var y = (l + 16) / 116, x = y + a / 500, z = y - b / 200; x = d3_lab_xyz(x) * d3_lab_X; y = d3_lab_xyz(y) * d3_lab_Y; z = d3_lab_xyz(z) * d3_lab_Z; return new d3_rgb(d3_xyz_rgb(3.2404542 * x - 1.5371385 * y - .4985314 * z), d3_xyz_rgb(-.969266 * x + 1.8760108 * y + .041556 * z), d3_xyz_rgb(.0556434 * x - .2040259 * y + 1.0572252 * z)); } function d3_lab_hcl(l, a, b) { return l > 0 ? new d3_hcl(Math.atan2(b, a) * d3_degrees, Math.sqrt(a * a + b * b), l) : new d3_hcl(NaN, NaN, l); } function d3_lab_xyz(x) { return x > .206893034 ? x * x * x : (x - 4 / 29) / 7.787037; } function d3_xyz_lab(x) { return x > .008856 ? Math.pow(x, 1 / 3) : 7.787037 * x + 4 / 29; } function d3_xyz_rgb(r) { return Math.round(255 * (r <= .00304 ? 12.92 * r : 1.055 * Math.pow(r, 1 / 2.4) - .055)); } d3.rgb = d3_rgb; function d3_rgb(r, g, b) { return this instanceof d3_rgb ? void (this.r = ~~r, this.g = ~~g, this.b = ~~b) : arguments.length < 2 ? r instanceof d3_rgb ? new d3_rgb(r.r, r.g, r.b) : d3_rgb_parse("" + r, d3_rgb, d3_hsl_rgb) : new d3_rgb(r, g, b); } function d3_rgbNumber(value) { return new d3_rgb(value >> 16, value >> 8 & 255, value & 255); } function d3_rgbString(value) { return d3_rgbNumber(value) + ""; } var d3_rgbPrototype = d3_rgb.prototype = new d3_color(); d3_rgbPrototype.brighter = function(k) { k = Math.pow(.7, arguments.length ? k : 1); var r = this.r, g = this.g, b = this.b, i = 30; if (!r && !g && !b) return new d3_rgb(i, i, i); if (r && r < i) r = i; if (g && g < i) g = i; if (b && b < i) b = i; return new d3_rgb(Math.min(255, r / k), Math.min(255, g / k), Math.min(255, b / k)); }; d3_rgbPrototype.darker = function(k) { k = Math.pow(.7, arguments.length ? k : 1); return new d3_rgb(k * this.r, k * this.g, k * this.b); }; d3_rgbPrototype.hsl = function() { return d3_rgb_hsl(this.r, this.g, this.b); }; d3_rgbPrototype.toString = function() { return "#" + d3_rgb_hex(this.r) + d3_rgb_hex(this.g) + d3_rgb_hex(this.b); }; function d3_rgb_hex(v) { return v < 16 ? "0" + Math.max(0, v).toString(16) : Math.min(255, v).toString(16); } function d3_rgb_parse(format, rgb, hsl) { var r = 0, g = 0, b = 0, m1, m2, color; m1 = /([a-z]+)\((.*)\)/.exec(format = format.toLowerCase()); if (m1) { m2 = m1[2].split(","); switch (m1[1]) { case "hsl": { return hsl(parseFloat(m2[0]), parseFloat(m2[1]) / 100, parseFloat(m2[2]) / 100); } case "rgb": { return rgb(d3_rgb_parseNumber(m2[0]), d3_rgb_parseNumber(m2[1]), d3_rgb_parseNumber(m2[2])); } } } if (color = d3_rgb_names.get(format)) { return rgb(color.r, color.g, color.b); } if (format != null && format.charAt(0) === "#" && !isNaN(color = parseInt(format.slice(1), 16))) { if (format.length === 4) { r = (color & 3840) >> 4; r = r >> 4 | r; g = color & 240; g = g >> 4 | g; b = color & 15; b = b << 4 | b; } else if (format.length === 7) { r = (color & 16711680) >> 16; g = (color & 65280) >> 8; b = color & 255; } } return rgb(r, g, b); } function d3_rgb_hsl(r, g, b) { var min = Math.min(r /= 255, g /= 255, b /= 255), max = Math.max(r, g, b), d = max - min, h, s, l = (max + min) / 2; if (d) { s = l < .5 ? d / (max + min) : d / (2 - max - min); if (r == max) h = (g - b) / d + (g < b ? 6 : 0); else if (g == max) h = (b - r) / d + 2; else h = (r - g) / d + 4; h *= 60; } else { h = NaN; s = l > 0 && l < 1 ? 0 : h; } return new d3_hsl(h, s, l); } function d3_rgb_lab(r, g, b) { r = d3_rgb_xyz(r); g = d3_rgb_xyz(g); b = d3_rgb_xyz(b); var x = d3_xyz_lab((.4124564 * r + .3575761 * g + .1804375 * b) / d3_lab_X), y = d3_xyz_lab((.2126729 * r + .7151522 * g + .072175 * b) / d3_lab_Y), z = d3_xyz_lab((.0193339 * r + .119192 * g + .9503041 * b) / d3_lab_Z); return d3_lab(116 * y - 16, 500 * (x - y), 200 * (y - z)); } function d3_rgb_xyz(r) { return (r /= 255) <= .04045 ? r / 12.92 : Math.pow((r + .055) / 1.055, 2.4); } function d3_rgb_parseNumber(c) { var f = parseFloat(c); return c.charAt(c.length - 1) === "%" ? Math.round(f * 2.55) : f; } var d3_rgb_names = d3.map({ aliceblue: 15792383, antiquewhite: 16444375, aqua: 65535, aquamarine: 8388564, azure: 15794175, beige: 16119260, bisque: 16770244, black: 0, blanchedalmond: 16772045, blue: 255, blueviolet: 9055202, brown: 10824234, burlywood: 14596231, cadetblue: 6266528, chartreuse: 8388352, chocolate: 13789470, coral: 16744272, cornflowerblue: 6591981, cornsilk: 16775388, crimson: 14423100, cyan: 65535, darkblue: 139, darkcyan: 35723, darkgoldenrod: 12092939, darkgray: 11119017, darkgreen: 25600, darkgrey: 11119017, darkkhaki: 12433259, darkmagenta: 9109643, darkolivegreen: 5597999, darkorange: 16747520, darkorchid: 10040012, darkred: 9109504, darksalmon: 15308410, darkseagreen: 9419919, darkslateblue: 4734347, darkslategray: 3100495, darkslategrey: 3100495, darkturquoise: 52945, darkviolet: 9699539, deeppink: 16716947, deepskyblue: 49151, dimgray: 6908265, dimgrey: 6908265, dodgerblue: 2003199, firebrick: 11674146, floralwhite: 16775920, forestgreen: 2263842, fuchsia: 16711935, gainsboro: 14474460, ghostwhite: 16316671, gold: 16766720, goldenrod: 14329120, gray: 8421504, green: 32768, greenyellow: 11403055, grey: 8421504, honeydew: 15794160, hotpink: 16738740, indianred: 13458524, indigo: 4915330, ivory: 16777200, khaki: 15787660, lavender: 15132410, lavenderblush: 16773365, lawngreen: 8190976, lemonchiffon: 16775885, lightblue: 11393254, lightcoral: 15761536, lightcyan: 14745599, lightgoldenrodyellow: 16448210, lightgray: 13882323, lightgreen: 9498256, lightgrey: 13882323, lightpink: 16758465, lightsalmon: 16752762, lightseagreen: 2142890, lightskyblue: 8900346, lightslategray: 7833753, lightslategrey: 7833753, lightsteelblue: 11584734, lightyellow: 16777184, lime: 65280, limegreen: 3329330, linen: 16445670, magenta: 16711935, maroon: 8388608, mediumaquamarine: 6737322, mediumblue: 205, mediumorchid: 12211667, mediumpurple: 9662683, mediumseagreen: 3978097, mediumslateblue: 8087790, mediumspringgreen: 64154, mediumturquoise: 4772300, mediumvioletred: 13047173, midnightblue: 1644912, mintcream: 16121850, mistyrose: 16770273, moccasin: 16770229, navajowhite: 16768685, navy: 128, oldlace: 16643558, olive: 8421376, olivedrab: 7048739, orange: 16753920, orangered: 16729344, orchid: 14315734, palegoldenrod: 15657130, palegreen: 10025880, paleturquoise: 11529966, palevioletred: 14381203, papayawhip: 16773077, peachpuff: 16767673, peru: 13468991, pink: 16761035, plum: 14524637, powderblue: 11591910, purple: 8388736, rebeccapurple: 6697881, red: 16711680, rosybrown: 12357519, royalblue: 4286945, saddlebrown: 9127187, salmon: 16416882, sandybrown: 16032864, seagreen: 3050327, seashell: 16774638, sienna: 10506797, silver: 12632256, skyblue: 8900331, slateblue: 6970061, slategray: 7372944, slategrey: 7372944, snow: 16775930, springgreen: 65407, steelblue: 4620980, tan: 13808780, teal: 32896, thistle: 14204888, tomato: 16737095, turquoise: 4251856, violet: 15631086, wheat: 16113331, white: 16777215, whitesmoke: 16119285, yellow: 16776960, yellowgreen: 10145074 }); d3_rgb_names.forEach(function(key, value) { d3_rgb_names.set(key, d3_rgbNumber(value)); }); function d3_functor(v) { return typeof v === "function" ? v : function() { return v; }; } d3.functor = d3_functor; d3.xhr = d3_xhrType(d3_identity); function d3_xhrType(response) { return function(url, mimeType, callback) { if (arguments.length === 2 && typeof mimeType === "function") callback = mimeType, mimeType = null; return d3_xhr(url, mimeType, response, callback); }; } function d3_xhr(url, mimeType, response, callback) { var xhr = {}, dispatch = d3.dispatch("beforesend", "progress", "load", "error"), headers = {}, request = new XMLHttpRequest(), responseType = null; if (this.XDomainRequest && !("withCredentials" in request) && /^(http(s)?:)?\/\//.test(url)) request = new XDomainRequest(); "onload" in request ? request.onload = request.onerror = respond : request.onreadystatechange = function() { request.readyState > 3 && respond(); }; function respond() { var status = request.status, result; if (!status && d3_xhrHasResponse(request) || status >= 200 && status < 300 || status === 304) { try { result = response.call(xhr, request); } catch (e) { dispatch.error.call(xhr, e); return; } dispatch.load.call(xhr, result); } else { dispatch.error.call(xhr, request); } } request.onprogress = function(event) { var o = d3.event; d3.event = event; try { dispatch.progress.call(xhr, request); } finally { d3.event = o; } }; xhr.header = function(name, value) { name = (name + "").toLowerCase(); if (arguments.length < 2) return headers[name]; if (value == null) delete headers[name]; else headers[name] = value + ""; return xhr; }; xhr.mimeType = function(value) { if (!arguments.length) return mimeType; mimeType = value == null ? null : value + ""; return xhr; }; xhr.responseType = function(value) { if (!arguments.length) return responseType; responseType = value; return xhr; }; xhr.response = function(value) { response = value; return xhr; }; [ "get", "post" ].forEach(function(method) { xhr[method] = function() { return xhr.send.apply(xhr, [ method ].concat(d3_array(arguments))); }; }); xhr.send = function(method, data, callback) { if (arguments.length === 2 && typeof data === "function") callback = data, data = null; request.open(method, url, true); if (mimeType != null && !("accept" in headers)) headers["accept"] = mimeType + ",*/*"; if (request.setRequestHeader) for (var name in headers) request.setRequestHeader(name, headers[name]); if (mimeType != null && request.overrideMimeType) request.overrideMimeType(mimeType); if (responseType != null) request.responseType = responseType; if (callback != null) xhr.on("error", callback).on("load", function(request) { callback(null, request); }); dispatch.beforesend.call(xhr, request); request.send(data == null ? null : data); return xhr; }; xhr.abort = function() { request.abort(); return xhr; }; d3.rebind(xhr, dispatch, "on"); return callback == null ? xhr : xhr.get(d3_xhr_fixCallback(callback)); } function d3_xhr_fixCallback(callback) { return callback.length === 1 ? function(error, request) { callback(error == null ? request : null); } : callback; } function d3_xhrHasResponse(request) { var type = request.responseType; return type && type !== "text" ? request.response : request.responseText; } d3.dsv = function(delimiter, mimeType) { var reFormat = new RegExp('["' + delimiter + "\n]"), delimiterCode = delimiter.charCodeAt(0); function dsv(url, row, callback) { if (arguments.length < 3) callback = row, row = null; var xhr = d3_xhr(url, mimeType, row == null ? response : typedResponse(row), callback); xhr.row = function(_) { return arguments.length ? xhr.response((row = _) == null ? response : typedResponse(_)) : row; }; return xhr; } function response(request) { return dsv.parse(request.responseText); } function typedResponse(f) { return function(request) { return dsv.parse(request.responseText, f); }; } dsv.parse = function(text, f) { var o; return dsv.parseRows(text, function(row, i) { if (o) return o(row, i - 1); var a = new Function("d", "return {" + row.map(function(name, i) { return JSON.stringify(name) + ": d[" + i + "]"; }).join(",") + "}"); o = f ? function(row, i) { return f(a(row), i); } : a; }); }; dsv.parseRows = function(text, f) { var EOL = {}, EOF = {}, rows = [], N = text.length, I = 0, n = 0, t, eol; function token() { if (I >= N) return EOF; if (eol) return eol = false, EOL; var j = I; if (text.charCodeAt(j) === 34) { var i = j; while (i++ < N) { if (text.charCodeAt(i) === 34) { if (text.charCodeAt(i + 1) !== 34) break; ++i; } } I = i + 2; var c = text.charCodeAt(i + 1); if (c === 13) { eol = true; if (text.charCodeAt(i + 2) === 10) ++I; } else if (c === 10) { eol = true; } return text.slice(j + 1, i).replace(/""/g, '"'); } while (I < N) { var c = text.charCodeAt(I++), k = 1; if (c === 10) eol = true; else if (c === 13) { eol = true; if (text.charCodeAt(I) === 10) ++I, ++k; } else if (c !== delimiterCode) continue; return text.slice(j, I - k); } return text.slice(j); } while ((t = token()) !== EOF) { var a = []; while (t !== EOL && t !== EOF) { a.push(t); t = token(); } if (f && (a = f(a, n++)) == null) continue; rows.push(a); } return rows; }; dsv.format = function(rows) { if (Array.isArray(rows[0])) return dsv.formatRows(rows); var fieldSet = new d3_Set(), fields = []; rows.forEach(function(row) { for (var field in row) { if (!fieldSet.has(field)) { fields.push(fieldSet.add(field)); } } }); return [ fields.map(formatValue).join(delimiter) ].concat(rows.map(function(row) { return fields.map(function(field) { return formatValue(row[field]); }).join(delimiter); })).join("\n"); }; dsv.formatRows = function(rows) { return rows.map(formatRow).join("\n"); }; function formatRow(row) { return row.map(formatValue).join(delimiter); } function formatValue(text) { return reFormat.test(text) ? '"' + text.replace(/\"/g, '""') + '"' : text; } return dsv; }; d3.csv = d3.dsv(",", "text/csv"); d3.tsv = d3.dsv(" ", "text/tab-separated-values"); var d3_timer_queueHead, d3_timer_queueTail, d3_timer_interval, d3_timer_timeout, d3_timer_frame = this[d3_vendorSymbol(this, "requestAnimationFrame")] || function(callback) { setTimeout(callback, 17); }; d3.timer = function() { d3_timer.apply(this, arguments); }; function d3_timer(callback, delay, then) { var n = arguments.length; if (n < 2) delay = 0; if (n < 3) then = Date.now(); var time = then + delay, timer = { c: callback, t: time, n: null }; if (d3_timer_queueTail) d3_timer_queueTail.n = timer; else d3_timer_queueHead = timer; d3_timer_queueTail = timer; if (!d3_timer_interval) { d3_timer_timeout = clearTimeout(d3_timer_timeout); d3_timer_interval = 1; d3_timer_frame(d3_timer_step); } return timer; } function d3_timer_step() { var now = d3_timer_mark(), delay = d3_timer_sweep() - now; if (delay > 24) { if (isFinite(delay)) { clearTimeout(d3_timer_timeout); d3_timer_timeout = setTimeout(d3_timer_step, delay); } d3_timer_interval = 0; } else { d3_timer_interval = 1; d3_timer_frame(d3_timer_step); } } d3.timer.flush = function() { d3_timer_mark(); d3_timer_sweep(); }; function d3_timer_mark() { var now = Date.now(), timer = d3_timer_queueHead; while (timer) { if (now >= timer.t && timer.c(now - timer.t)) timer.c = null; timer = timer.n; } return now; } function d3_timer_sweep() { var t0, t1 = d3_timer_queueHead, time = Infinity; while (t1) { if (t1.c) { if (t1.t < time) time = t1.t; t1 = (t0 = t1).n; } else { t1 = t0 ? t0.n = t1.n : d3_timer_queueHead = t1.n; } } d3_timer_queueTail = t0; return time; } function d3_format_precision(x, p) { return p - (x ? Math.ceil(Math.log(x) / Math.LN10) : 1); } d3.round = function(x, n) { return n ? Math.round(x * (n = Math.pow(10, n))) / n : Math.round(x); }; var d3_formatPrefixes = [ "y", "z", "a", "f", "p", "n", "µ", "m", "", "k", "M", "G", "T", "P", "E", "Z", "Y" ].map(d3_formatPrefix); d3.formatPrefix = function(value, precision) { var i = 0; if (value = +value) { if (value < 0) value *= -1; if (precision) value = d3.round(value, d3_format_precision(value, precision)); i = 1 + Math.floor(1e-12 + Math.log(value) / Math.LN10); i = Math.max(-24, Math.min(24, Math.floor((i - 1) / 3) * 3)); } return d3_formatPrefixes[8 + i / 3]; }; function d3_formatPrefix(d, i) { var k = Math.pow(10, abs(8 - i) * 3); return { scale: i > 8 ? function(d) { return d / k; } : function(d) { return d * k; }, symbol: d }; } function d3_locale_numberFormat(locale) { var locale_decimal = locale.decimal, locale_thousands = locale.thousands, locale_grouping = locale.grouping, locale_currency = locale.currency, formatGroup = locale_grouping && locale_thousands ? function(value, width) { var i = value.length, t = [], j = 0, g = locale_grouping[0], length = 0; while (i > 0 && g > 0) { if (length + g + 1 > width) g = Math.max(1, width - length); t.push(value.substring(i -= g, i + g)); if ((length += g + 1) > width) break; g = locale_grouping[j = (j + 1) % locale_grouping.length]; } return t.reverse().join(locale_thousands); } : d3_identity; return function(specifier) { var match = d3_format_re.exec(specifier), fill = match[1] || " ", align = match[2] || ">", sign = match[3] || "-", symbol = match[4] || "", zfill = match[5], width = +match[6], comma = match[7], precision = match[8], type = match[9], scale = 1, prefix = "", suffix = "", integer = false, exponent = true; if (precision) precision = +precision.substring(1); if (zfill || fill === "0" && align === "=") { zfill = fill = "0"; align = "="; } switch (type) { case "n": comma = true; type = "g"; break; case "%": scale = 100; suffix = "%"; type = "f"; break; case "p": scale = 100; suffix = "%"; type = "r"; break; case "b": case "o": case "x": case "X": if (symbol === "#") prefix = "0" + type.toLowerCase(); case "c": exponent = false; case "d": integer = true; precision = 0; break; case "s": scale = -1; type = "r"; break; } if (symbol === "$") prefix = locale_currency[0], suffix = locale_currency[1]; if (type == "r" && !precision) type = "g"; if (precision != null) { if (type == "g") precision = Math.max(1, Math.min(21, precision)); else if (type == "e" || type == "f") precision = Math.max(0, Math.min(20, precision)); } type = d3_format_types.get(type) || d3_format_typeDefault; var zcomma = zfill && comma; return function(value) { var fullSuffix = suffix; if (integer && value % 1) return ""; var negative = value < 0 || value === 0 && 1 / value < 0 ? (value = -value, "-") : sign === "-" ? "" : sign; if (scale < 0) { var unit = d3.formatPrefix(value, precision); value = unit.scale(value); fullSuffix = unit.symbol + suffix; } else { value *= scale; } value = type(value, precision); var i = value.lastIndexOf("."), before, after; if (i < 0) { var j = exponent ? value.lastIndexOf("e") : -1; if (j < 0) before = value, after = ""; else before = value.substring(0, j), after = value.substring(j); } else { before = value.substring(0, i); after = locale_decimal + value.substring(i + 1); } if (!zfill && comma) before = formatGroup(before, Infinity); var length = prefix.length + before.length + after.length + (zcomma ? 0 : negative.length), padding = length < width ? new Array(length = width - length + 1).join(fill) : ""; if (zcomma) before = formatGroup(padding + before, padding.length ? width - after.length : Infinity); negative += prefix; value = before + after; return (align === "<" ? negative + value + padding : align === ">" ? padding + negative + value : align === "^" ? padding.substring(0, length >>= 1) + negative + value + padding.substring(length) : negative + (zcomma ? value : padding + value)) + fullSuffix; }; }; } var d3_format_re = /(?:([^{])?([<>=^]))?([+\- ])?([$#])?(0)?(\d+)?(,)?(\.-?\d+)?([a-z%])?/i; var d3_format_types = d3.map({ b: function(x) { return x.toString(2); }, c: function(x) { return String.fromCharCode(x); }, o: function(x) { return x.toString(8); }, x: function(x) { return x.toString(16); }, X: function(x) { return x.toString(16).toUpperCase(); }, g: function(x, p) { return x.toPrecision(p); }, e: function(x, p) { return x.toExponential(p); }, f: function(x, p) { return x.toFixed(p); }, r: function(x, p) { return (x = d3.round(x, d3_format_precision(x, p))).toFixed(Math.max(0, Math.min(20, d3_format_precision(x * (1 + 1e-15), p)))); } }); function d3_format_typeDefault(x) { return x + ""; } var d3_time = d3.time = {}, d3_date = Date; function d3_date_utc() { this._ = new Date(arguments.length > 1 ? Date.UTC.apply(this, arguments) : arguments[0]); } d3_date_utc.prototype = { getDate: function() { return this._.getUTCDate(); }, getDay: function() { return this._.getUTCDay(); }, getFullYear: function() { return this._.getUTCFullYear(); }, getHours: function() { return this._.getUTCHours(); }, getMilliseconds: function() { return this._.getUTCMilliseconds(); }, getMinutes: function() { return this._.getUTCMinutes(); }, getMonth: function() { return this._.getUTCMonth(); }, getSeconds: function() { return this._.getUTCSeconds(); }, getTime: function() { return this._.getTime(); }, getTimezoneOffset: function() { return 0; }, valueOf: function() { return this._.valueOf(); }, setDate: function() { d3_time_prototype.setUTCDate.apply(this._, arguments); }, setDay: function() { d3_time_prototype.setUTCDay.apply(this._, arguments); }, setFullYear: function() { d3_time_prototype.setUTCFullYear.apply(this._, arguments); }, setHours: function() { d3_time_prototype.setUTCHours.apply(this._, arguments); }, setMilliseconds: function() { d3_time_prototype.setUTCMilliseconds.apply(this._, arguments); }, setMinutes: function() { d3_time_prototype.setUTCMinutes.apply(this._, arguments); }, setMonth: function() { d3_time_prototype.setUTCMonth.apply(this._, arguments); }, setSeconds: function() { d3_time_prototype.setUTCSeconds.apply(this._, arguments); }, setTime: function() { d3_time_prototype.setTime.apply(this._, arguments); } }; var d3_time_prototype = Date.prototype; function d3_time_interval(local, step, number) { function round(date) { var d0 = local(date), d1 = offset(d0, 1); return date - d0 < d1 - date ? d0 : d1; } function ceil(date) { step(date = local(new d3_date(date - 1)), 1); return date; } function offset(date, k) { step(date = new d3_date(+date), k); return date; } function range(t0, t1, dt) { var time = ceil(t0), times = []; if (dt > 1) { while (time < t1) { if (!(number(time) % dt)) times.push(new Date(+time)); step(time, 1); } } else { while (time < t1) times.push(new Date(+time)), step(time, 1); } return times; } function range_utc(t0, t1, dt) { try { d3_date = d3_date_utc; var utc = new d3_date_utc(); utc._ = t0; return range(utc, t1, dt); } finally { d3_date = Date; } } local.floor = local; local.round = round; local.ceil = ceil; local.offset = offset; local.range = range; var utc = local.utc = d3_time_interval_utc(local); utc.floor = utc; utc.round = d3_time_interval_utc(round); utc.ceil = d3_time_interval_utc(ceil); utc.offset = d3_time_interval_utc(offset); utc.range = range_utc; return local; } function d3_time_interval_utc(method) { return function(date, k) { try { d3_date = d3_date_utc; var utc = new d3_date_utc(); utc._ = date; return method(utc, k)._; } finally { d3_date = Date; } }; } d3_time.year = d3_time_interval(function(date) { date = d3_time.day(date); date.setMonth(0, 1); return date; }, function(date, offset) { date.setFullYear(date.getFullYear() + offset); }, function(date) { return date.getFullYear(); }); d3_time.years = d3_time.year.range; d3_time.years.utc = d3_time.year.utc.range; d3_time.day = d3_time_interval(function(date) { var day = new d3_date(2e3, 0); day.setFullYear(date.getFullYear(), date.getMonth(), date.getDate()); return day; }, function(date, offset) { date.setDate(date.getDate() + offset); }, function(date) { return date.getDate() - 1; }); d3_time.days = d3_time.day.range; d3_time.days.utc = d3_time.day.utc.range; d3_time.dayOfYear = function(date) { var year = d3_time.year(date); return Math.floor((date - year - (date.getTimezoneOffset() - year.getTimezoneOffset()) * 6e4) / 864e5); }; [ "sunday", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday" ].forEach(function(day, i) { i = 7 - i; var interval = d3_time[day] = d3_time_interval(function(date) { (date = d3_time.day(date)).setDate(date.getDate() - (date.getDay() + i) % 7); return date; }, function(date, offset) { date.setDate(date.getDate() + Math.floor(offset) * 7); }, function(date) { var day = d3_time.year(date).getDay(); return Math.floor((d3_time.dayOfYear(date) + (day + i) % 7) / 7) - (day !== i); }); d3_time[day + "s"] = interval.range; d3_time[day + "s"].utc = interval.utc.range; d3_time[day + "OfYear"] = function(date) { var day = d3_time.year(date).getDay(); return Math.floor((d3_time.dayOfYear(date) + (day + i) % 7) / 7); }; }); d3_time.week = d3_time.sunday; d3_time.weeks = d3_time.sunday.range; d3_time.weeks.utc = d3_time.sunday.utc.range; d3_time.weekOfYear = d3_time.sundayOfYear; function d3_locale_timeFormat(locale) { var locale_dateTime = locale.dateTime, locale_date = locale.date, locale_time = locale.time, locale_periods = locale.periods, locale_days = locale.days, locale_shortDays = locale.shortDays, locale_months = locale.months, locale_shortMonths = locale.shortMonths; function d3_time_format(template) { var n = template.length; function format(date) { var string = [], i = -1, j = 0, c, p, f; while (++i < n) { if (template.charCodeAt(i) === 37) { string.push(template.slice(j, i)); if ((p = d3_time_formatPads[c = template.charAt(++i)]) != null) c = template.charAt(++i); if (f = d3_time_formats[c]) c = f(date, p == null ? c === "e" ? " " : "0" : p); string.push(c); j = i + 1; } } string.push(template.slice(j, i)); return string.join(""); } format.parse = function(string) { var d = { y: 1900, m: 0, d: 1, H: 0, M: 0, S: 0, L: 0, Z: null }, i = d3_time_parse(d, template, string, 0); if (i != string.length) return null; if ("p" in d) d.H = d.H % 12 + d.p * 12; var localZ = d.Z != null && d3_date !== d3_date_utc, date = new (localZ ? d3_date_utc : d3_date)(); if ("j" in d) date.setFullYear(d.y, 0, d.j); else if ("W" in d || "U" in d) { if (!("w" in d)) d.w = "W" in d ? 1 : 0; date.setFullYear(d.y, 0, 1); date.setFullYear(d.y, 0, "W" in d ? (d.w + 6) % 7 + d.W * 7 - (date.getDay() + 5) % 7 : d.w + d.U * 7 - (date.getDay() + 6) % 7); } else date.setFullYear(d.y, d.m, d.d); date.setHours(d.H + (d.Z / 100 | 0), d.M + d.Z % 100, d.S, d.L); return localZ ? date._ : date; }; format.toString = function() { return template; }; return format; } function d3_time_parse(date, template, string, j) { var c, p, t, i = 0, n = template.length, m = string.length; while (i < n) { if (j >= m) return -1; c = template.charCodeAt(i++); if (c === 37) { t = template.charAt(i++); p = d3_time_parsers[t in d3_time_formatPads ? template.charAt(i++) : t]; if (!p || (j = p(date, string, j)) < 0) return -1; } else if (c != string.charCodeAt(j++)) { return -1; } } return j; } d3_time_format.utc = function(template) { var local = d3_time_format(template); function format(date) { try { d3_date = d3_date_utc; var utc = new d3_date(); utc._ = date; return local(utc); } finally { d3_date = Date; } } format.parse = function(string) { try { d3_date = d3_date_utc; var date = local.parse(string); return date && date._; } finally { d3_date = Date; } }; format.toString = local.toString; return format; }; d3_time_format.multi = d3_time_format.utc.multi = d3_time_formatMulti; var d3_time_periodLookup = d3.map(), d3_time_dayRe = d3_time_formatRe(locale_days), d3_time_dayLookup = d3_time_formatLookup(locale_days), d3_time_dayAbbrevRe = d3_time_formatRe(locale_shortDays), d3_time_dayAbbrevLookup = d3_time_formatLookup(locale_shortDays), d3_time_monthRe = d3_time_formatRe(locale_months), d3_time_monthLookup = d3_time_formatLookup(locale_months), d3_time_monthAbbrevRe = d3_time_formatRe(locale_shortMonths), d3_time_monthAbbrevLookup = d3_time_formatLookup(locale_shortMonths); locale_periods.forEach(function(p, i) { d3_time_periodLookup.set(p.toLowerCase(), i); }); var d3_time_formats = { a: function(d) { return locale_shortDays[d.getDay()]; }, A: function(d) { return locale_days[d.getDay()]; }, b: function(d) { return locale_shortMonths[d.getMonth()]; }, B: function(d) { return locale_months[d.getMonth()]; }, c: d3_time_format(locale_dateTime), d: function(d, p) { return d3_time_formatPad(d.getDate(), p, 2); }, e: function(d, p) { return d3_time_formatPad(d.getDate(), p, 2); }, H: function(d, p) { return d3_time_formatPad(d.getHours(), p, 2); }, I: function(d, p) { return d3_time_formatPad(d.getHours() % 12 || 12, p, 2); }, j: function(d, p) { return d3_time_formatPad(1 + d3_time.dayOfYear(d), p, 3); }, L: function(d, p) { return d3_time_formatPad(d.getMilliseconds(), p, 3); }, m: function(d, p) { return d3_time_formatPad(d.getMonth() + 1, p, 2); }, M: function(d, p) { return d3_time_formatPad(d.getMinutes(), p, 2); }, p: function(d) { return locale_periods[+(d.getHours() >= 12)]; }, S: function(d, p) { return d3_time_formatPad(d.getSeconds(), p, 2); }, U: function(d, p) { return d3_time_formatPad(d3_time.sundayOfYear(d), p, 2); }, w: function(d) { return d.getDay(); }, W: function(d, p) { return d3_time_formatPad(d3_time.mondayOfYear(d), p, 2); }, x: d3_time_format(locale_date), X: d3_time_format(locale_time), y: function(d, p) { return d3_time_formatPad(d.getFullYear() % 100, p, 2); }, Y: function(d, p) { return d3_time_formatPad(d.getFullYear() % 1e4, p, 4); }, Z: d3_time_zone, "%": function() { return "%"; } }; var d3_time_parsers = { a: d3_time_parseWeekdayAbbrev, A: d3_time_parseWeekday, b: d3_time_parseMonthAbbrev, B: d3_time_parseMonth, c: d3_time_parseLocaleFull, d: d3_time_parseDay, e: d3_time_parseDay, H: d3_time_parseHour24, I: d3_time_parseHour24, j: d3_time_parseDayOfYear, L: d3_time_parseMilliseconds, m: d3_time_parseMonthNumber, M: d3_time_parseMinutes, p: d3_time_parseAmPm, S: d3_time_parseSeconds, U: d3_time_parseWeekNumberSunday, w: d3_time_parseWeekdayNumber, W: d3_time_parseWeekNumberMonday, x: d3_time_parseLocaleDate, X: d3_time_parseLocaleTime, y: d3_time_parseYear, Y: d3_time_parseFullYear, Z: d3_time_parseZone, "%": d3_time_parseLiteralPercent }; function d3_time_parseWeekdayAbbrev(date, string, i) { d3_time_dayAbbrevRe.lastIndex = 0; var n = d3_time_dayAbbrevRe.exec(string.slice(i)); return n ? (date.w = d3_time_dayAbbrevLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; } function d3_time_parseWeekday(date, string, i) { d3_time_dayRe.lastIndex = 0; var n = d3_time_dayRe.exec(string.slice(i)); return n ? (date.w = d3_time_dayLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; } function d3_time_parseMonthAbbrev(date, string, i) { d3_time_monthAbbrevRe.lastIndex = 0; var n = d3_time_monthAbbrevRe.exec(string.slice(i)); return n ? (date.m = d3_time_monthAbbrevLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; } function d3_time_parseMonth(date, string, i) { d3_time_monthRe.lastIndex = 0; var n = d3_time_monthRe.exec(string.slice(i)); return n ? (date.m = d3_time_monthLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; } function d3_time_parseLocaleFull(date, string, i) { return d3_time_parse(date, d3_time_formats.c.toString(), string, i); } function d3_time_parseLocaleDate(date, string, i) { return d3_time_parse(date, d3_time_formats.x.toString(), string, i); } function d3_time_parseLocaleTime(date, string, i) { return d3_time_parse(date, d3_time_formats.X.toString(), string, i); } function d3_time_parseAmPm(date, string, i) { var n = d3_time_periodLookup.get(string.slice(i, i += 2).toLowerCase()); return n == null ? -1 : (date.p = n, i); } return d3_time_format; } var d3_time_formatPads = { "-": "", _: " ", "0": "0" }, d3_time_numberRe = /^\s*\d+/, d3_time_percentRe = /^%/; function d3_time_formatPad(value, fill, width) { var sign = value < 0 ? "-" : "", string = (sign ? -value : value) + "", length = string.length; return sign + (length < width ? new Array(width - length + 1).join(fill) + string : string); } function d3_time_formatRe(names) { return new RegExp("^(?:" + names.map(d3.requote).join("|") + ")", "i"); } function d3_time_formatLookup(names) { var map = new d3_Map(), i = -1, n = names.length; while (++i < n) map.set(names[i].toLowerCase(), i); return map; } function d3_time_parseWeekdayNumber(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 1)); return n ? (date.w = +n[0], i + n[0].length) : -1; } function d3_time_parseWeekNumberSunday(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i)); return n ? (date.U = +n[0], i + n[0].length) : -1; } function d3_time_parseWeekNumberMonday(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i)); return n ? (date.W = +n[0], i + n[0].length) : -1; } function d3_time_parseFullYear(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 4)); return n ? (date.y = +n[0], i + n[0].length) : -1; } function d3_time_parseYear(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 2)); return n ? (date.y = d3_time_expandYear(+n[0]), i + n[0].length) : -1; } function d3_time_parseZone(date, string, i) { return /^[+-]\d{4}$/.test(string = string.slice(i, i + 5)) ? (date.Z = -string, i + 5) : -1; } function d3_time_expandYear(d) { return d + (d > 68 ? 1900 : 2e3); } function d3_time_parseMonthNumber(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 2)); return n ? (date.m = n[0] - 1, i + n[0].length) : -1; } function d3_time_parseDay(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 2)); return n ? (date.d = +n[0], i + n[0].length) : -1; } function d3_time_parseDayOfYear(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 3)); return n ? (date.j = +n[0], i + n[0].length) : -1; } function d3_time_parseHour24(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 2)); return n ? (date.H = +n[0], i + n[0].length) : -1; } function d3_time_parseMinutes(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 2)); return n ? (date.M = +n[0], i + n[0].length) : -1; } function d3_time_parseSeconds(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 2)); return n ? (date.S = +n[0], i + n[0].length) : -1; } function d3_time_parseMilliseconds(date, string, i) { d3_time_numberRe.lastIndex = 0; var n = d3_time_numberRe.exec(string.slice(i, i + 3)); return n ? (date.L = +n[0], i + n[0].length) : -1; } function d3_time_zone(d) { var z = d.getTimezoneOffset(), zs = z > 0 ? "-" : "+", zh = abs(z) / 60 | 0, zm = abs(z) % 60; return zs + d3_time_formatPad(zh, "0", 2) + d3_time_formatPad(zm, "0", 2); } function d3_time_parseLiteralPercent(date, string, i) { d3_time_percentRe.lastIndex = 0; var n = d3_time_percentRe.exec(string.slice(i, i + 1)); return n ? i + n[0].length : -1; } function d3_time_formatMulti(formats) { var n = formats.length, i = -1; while (++i < n) formats[i][0] = this(formats[i][0]); return function(date) { var i = 0, f = formats[i]; while (!f[1](date)) f = formats[++i]; return f[0](date); }; } d3.locale = function(locale) { return { numberFormat: d3_locale_numberFormat(locale), timeFormat: d3_locale_timeFormat(locale) }; }; var d3_locale_enUS = d3.locale({ decimal: ".", thousands: ",", grouping: [ 3 ], currency: [ "$", "" ], dateTime: "%a %b %e %X %Y", date: "%m/%d/%Y", time: "%H:%M:%S", periods: [ "AM", "PM" ], days: [ "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday" ], shortDays: [ "Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat" ], months: [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ], shortMonths: [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ] }); d3.format = d3_locale_enUS.numberFormat; d3.geo = {}; function d3_adder() {} d3_adder.prototype = { s: 0, t: 0, add: function(y) { d3_adderSum(y, this.t, d3_adderTemp); d3_adderSum(d3_adderTemp.s, this.s, this); if (this.s) this.t += d3_adderTemp.t; else this.s = d3_adderTemp.t; }, reset: function() { this.s = this.t = 0; }, valueOf: function() { return this.s; } }; var d3_adderTemp = new d3_adder(); function d3_adderSum(a, b, o) { var x = o.s = a + b, bv = x - a, av = x - bv; o.t = a - av + (b - bv); } d3.geo.stream = function(object, listener) { if (object && d3_geo_streamObjectType.hasOwnProperty(object.type)) { d3_geo_streamObjectType[object.type](object, listener); } else { d3_geo_streamGeometry(object, listener); } }; function d3_geo_streamGeometry(geometry, listener) { if (geometry && d3_geo_streamGeometryType.hasOwnProperty(geometry.type)) { d3_geo_streamGeometryType[geometry.type](geometry, listener); } } var d3_geo_streamObjectType = { Feature: function(feature, listener) { d3_geo_streamGeometry(feature.geometry, listener); }, FeatureCollection: function(object, listener) { var features = object.features, i = -1, n = features.length; while (++i < n) d3_geo_streamGeometry(features[i].geometry, listener); } }; var d3_geo_streamGeometryType = { Sphere: function(object, listener) { listener.sphere(); }, Point: function(object, listener) { object = object.coordinates; listener.point(object[0], object[1], object[2]); }, MultiPoint: function(object, listener) { var coordinates = object.coordinates, i = -1, n = coordinates.length; while (++i < n) object = coordinates[i], listener.point(object[0], object[1], object[2]); }, LineString: function(object, listener) { d3_geo_streamLine(object.coordinates, listener, 0); }, MultiLineString: function(object, listener) { var coordinates = object.coordinates, i = -1, n = coordinates.length; while (++i < n) d3_geo_streamLine(coordinates[i], listener, 0); }, Polygon: function(object, listener) { d3_geo_streamPolygon(object.coordinates, listener); }, MultiPolygon: function(object, listener) { var coordinates = object.coordinates, i = -1, n = coordinates.length; while (++i < n) d3_geo_streamPolygon(coordinates[i], listener); }, GeometryCollection: function(object, listener) { var geometries = object.geometries, i = -1, n = geometries.length; while (++i < n) d3_geo_streamGeometry(geometries[i], listener); } }; function d3_geo_streamLine(coordinates, listener, closed) { var i = -1, n = coordinates.length - closed, coordinate; listener.lineStart(); while (++i < n) coordinate = coordinates[i], listener.point(coordinate[0], coordinate[1], coordinate[2]); listener.lineEnd(); } function d3_geo_streamPolygon(coordinates, listener) { var i = -1, n = coordinates.length; listener.polygonStart(); while (++i < n) d3_geo_streamLine(coordinates[i], listener, 1); listener.polygonEnd(); } d3.geo.area = function(object) { d3_geo_areaSum = 0; d3.geo.stream(object, d3_geo_area); return d3_geo_areaSum; }; var d3_geo_areaSum, d3_geo_areaRingSum = new d3_adder(); var d3_geo_area = { sphere: function() { d3_geo_areaSum += 4 * π; }, point: d3_noop, lineStart: d3_noop, lineEnd: d3_noop, polygonStart: function() { d3_geo_areaRingSum.reset(); d3_geo_area.lineStart = d3_geo_areaRingStart; }, polygonEnd: function() { var area = 2 * d3_geo_areaRingSum; d3_geo_areaSum += area < 0 ? 4 * π + area : area; d3_geo_area.lineStart = d3_geo_area.lineEnd = d3_geo_area.point = d3_noop; } }; function d3_geo_areaRingStart() { var λ00, φ00, λ0, cosφ0, sinφ0; d3_geo_area.point = function(λ, φ) { d3_geo_area.point = nextPoint; λ0 = (λ00 = λ) * d3_radians, cosφ0 = Math.cos(φ = (φ00 = φ) * d3_radians / 2 + π / 4), sinφ0 = Math.sin(φ); }; function nextPoint(λ, φ) { λ *= d3_radians; φ = φ * d3_radians / 2 + π / 4; var dλ = λ - λ0, sdλ = dλ >= 0 ? 1 : -1, adλ = sdλ * dλ, cosφ = Math.cos(φ), sinφ = Math.sin(φ), k = sinφ0 * sinφ, u = cosφ0 * cosφ + k * Math.cos(adλ), v = k * sdλ * Math.sin(adλ); d3_geo_areaRingSum.add(Math.atan2(v, u)); λ0 = λ, cosφ0 = cosφ, sinφ0 = sinφ; } d3_geo_area.lineEnd = function() { nextPoint(λ00, φ00); }; } function d3_geo_cartesian(spherical) { var λ = spherical[0], φ = spherical[1], cosφ = Math.cos(φ); return [ cosφ * Math.cos(λ), cosφ * Math.sin(λ), Math.sin(φ) ]; } function d3_geo_cartesianDot(a, b) { return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]; } function d3_geo_cartesianCross(a, b) { return [ a[1] * b[2] - a[2] * b[1], a[2] * b[0] - a[0] * b[2], a[0] * b[1] - a[1] * b[0] ]; } function d3_geo_cartesianAdd(a, b) { a[0] += b[0]; a[1] += b[1]; a[2] += b[2]; } function d3_geo_cartesianScale(vector, k) { return [ vector[0] * k, vector[1] * k, vector[2] * k ]; } function d3_geo_cartesianNormalize(d) { var l = Math.sqrt(d[0] * d[0] + d[1] * d[1] + d[2] * d[2]); d[0] /= l; d[1] /= l; d[2] /= l; } function d3_geo_spherical(cartesian) { return [ Math.atan2(cartesian[1], cartesian[0]), d3_asin(cartesian[2]) ]; } function d3_geo_sphericalEqual(a, b) { return abs(a[0] - b[0]) < ε && abs(a[1] - b[1]) < ε; } d3.geo.bounds = function() { var λ0, φ0, λ1, φ1, λ_, λ__, φ__, p0, dλSum, ranges, range; var bound = { point: point, lineStart: lineStart, lineEnd: lineEnd, polygonStart: function() { bound.point = ringPoint; bound.lineStart = ringStart; bound.lineEnd = ringEnd; dλSum = 0; d3_geo_area.polygonStart(); }, polygonEnd: function() { d3_geo_area.polygonEnd(); bound.point = point; bound.lineStart = lineStart; bound.lineEnd = lineEnd; if (d3_geo_areaRingSum < 0) λ0 = -(λ1 = 180), φ0 = -(φ1 = 90); else if (dλSum > ε) φ1 = 90; else if (dλSum < -ε) φ0 = -90; range[0] = λ0, range[1] = λ1; } }; function point(λ, φ) { ranges.push(range = [ λ0 = λ, λ1 = λ ]); if (φ < φ0) φ0 = φ; if (φ > φ1) φ1 = φ; } function linePoint(λ, φ) { var p = d3_geo_cartesian([ λ * d3_radians, φ * d3_radians ]); if (p0) { var normal = d3_geo_cartesianCross(p0, p), equatorial = [ normal[1], -normal[0], 0 ], inflection = d3_geo_cartesianCross(equatorial, normal); d3_geo_cartesianNormalize(inflection); inflection = d3_geo_spherical(inflection); var dλ = λ - λ_, s = dλ > 0 ? 1 : -1, λi = inflection[0] * d3_degrees * s, antimeridian = abs(dλ) > 180; if (antimeridian ^ (s * λ_ < λi && λi < s * λ)) { var φi = inflection[1] * d3_degrees; if (φi > φ1) φ1 = φi; } else if (λi = (λi + 360) % 360 - 180, antimeridian ^ (s * λ_ < λi && λi < s * λ)) { var φi = -inflection[1] * d3_degrees; if (φi < φ0) φ0 = φi; } else { if (φ < φ0) φ0 = φ; if (φ > φ1) φ1 = φ; } if (antimeridian) { if (λ < λ_) { if (angle(λ0, λ) > angle(λ0, λ1)) λ1 = λ; } else { if (angle(λ, λ1) > angle(λ0, λ1)) λ0 = λ; } } else { if (λ1 >= λ0) { if (λ < λ0) λ0 = λ; if (λ > λ1) λ1 = λ; } else { if (λ > λ_) { if (angle(λ0, λ) > angle(λ0, λ1)) λ1 = λ; } else { if (angle(λ, λ1) > angle(λ0, λ1)) λ0 = λ; } } } } else { point(λ, φ); } p0 = p, λ_ = λ; } function lineStart() { bound.point = linePoint; } function lineEnd() { range[0] = λ0, range[1] = λ1; bound.point = point; p0 = null; } function ringPoint(λ, φ) { if (p0) { var dλ = λ - λ_; dλSum += abs(dλ) > 180 ? dλ + (dλ > 0 ? 360 : -360) : dλ; } else λ__ = λ, φ__ = φ; d3_geo_area.point(λ, φ); linePoint(λ, φ); } function ringStart() { d3_geo_area.lineStart(); } function ringEnd() { ringPoint(λ__, φ__); d3_geo_area.lineEnd(); if (abs(dλSum) > ε) λ0 = -(λ1 = 180); range[0] = λ0, range[1] = λ1; p0 = null; } function angle(λ0, λ1) { return (λ1 -= λ0) < 0 ? λ1 + 360 : λ1; } function compareRanges(a, b) { return a[0] - b[0]; } function withinRange(x, range) { return range[0] <= range[1] ? range[0] <= x && x <= range[1] : x < range[0] || range[1] < x; } return function(feature) { φ1 = λ1 = -(λ0 = φ0 = Infinity); ranges = []; d3.geo.stream(feature, bound); var n = ranges.length; if (n) { ranges.sort(compareRanges); for (var i = 1, a = ranges[0], b, merged = [ a ]; i < n; ++i) { b = ranges[i]; if (withinRange(b[0], a) || withinRange(b[1], a)) { if (angle(a[0], b[1]) > angle(a[0], a[1])) a[1] = b[1]; if (angle(b[0], a[1]) > angle(a[0], a[1])) a[0] = b[0]; } else { merged.push(a = b); } } var best = -Infinity, dλ; for (var n = merged.length - 1, i = 0, a = merged[n], b; i <= n; a = b, ++i) { b = merged[i]; if ((dλ = angle(a[1], b[0])) > best) best = dλ, λ0 = b[0], λ1 = a[1]; } } ranges = range = null; return λ0 === Infinity || φ0 === Infinity ? [ [ NaN, NaN ], [ NaN, NaN ] ] : [ [ λ0, φ0 ], [ λ1, φ1 ] ]; }; }(); d3.geo.centroid = function(object) { d3_geo_centroidW0 = d3_geo_centroidW1 = d3_geo_centroidX0 = d3_geo_centroidY0 = d3_geo_centroidZ0 = d3_geo_centroidX1 = d3_geo_centroidY1 = d3_geo_centroidZ1 = d3_geo_centroidX2 = d3_geo_centroidY2 = d3_geo_centroidZ2 = 0; d3.geo.stream(object, d3_geo_centroid); var x = d3_geo_centroidX2, y = d3_geo_centroidY2, z = d3_geo_centroidZ2, m = x * x + y * y + z * z; if (m < ε2) { x = d3_geo_centroidX1, y = d3_geo_centroidY1, z = d3_geo_centroidZ1; if (d3_geo_centroidW1 < ε) x = d3_geo_centroidX0, y = d3_geo_centroidY0, z = d3_geo_centroidZ0; m = x * x + y * y + z * z; if (m < ε2) return [ NaN, NaN ]; } return [ Math.atan2(y, x) * d3_degrees, d3_asin(z / Math.sqrt(m)) * d3_degrees ]; }; var d3_geo_centroidW0, d3_geo_centroidW1, d3_geo_centroidX0, d3_geo_centroidY0, d3_geo_centroidZ0, d3_geo_centroidX1, d3_geo_centroidY1, d3_geo_centroidZ1, d3_geo_centroidX2, d3_geo_centroidY2, d3_geo_centroidZ2; var d3_geo_centroid = { sphere: d3_noop, point: d3_geo_centroidPoint, lineStart: d3_geo_centroidLineStart, lineEnd: d3_geo_centroidLineEnd, polygonStart: function() { d3_geo_centroid.lineStart = d3_geo_centroidRingStart; }, polygonEnd: function() { d3_geo_centroid.lineStart = d3_geo_centroidLineStart; } }; function d3_geo_centroidPoint(λ, φ) { λ *= d3_radians; var cosφ = Math.cos(φ *= d3_radians); d3_geo_centroidPointXYZ(cosφ * Math.cos(λ), cosφ * Math.sin(λ), Math.sin(φ)); } function d3_geo_centroidPointXYZ(x, y, z) { ++d3_geo_centroidW0; d3_geo_centroidX0 += (x - d3_geo_centroidX0) / d3_geo_centroidW0; d3_geo_centroidY0 += (y - d3_geo_centroidY0) / d3_geo_centroidW0; d3_geo_centroidZ0 += (z - d3_geo_centroidZ0) / d3_geo_centroidW0; } function d3_geo_centroidLineStart() { var x0, y0, z0; d3_geo_centroid.point = function(λ, φ) { λ *= d3_radians; var cosφ = Math.cos(φ *= d3_radians); x0 = cosφ * Math.cos(λ); y0 = cosφ * Math.sin(λ); z0 = Math.sin(φ); d3_geo_centroid.point = nextPoint; d3_geo_centroidPointXYZ(x0, y0, z0); }; function nextPoint(λ, φ) { λ *= d3_radians; var cosφ = Math.cos(φ *= d3_radians), x = cosφ * Math.cos(λ), y = cosφ * Math.sin(λ), z = Math.sin(φ), w = Math.atan2(Math.sqrt((w = y0 * z - z0 * y) * w + (w = z0 * x - x0 * z) * w + (w = x0 * y - y0 * x) * w), x0 * x + y0 * y + z0 * z); d3_geo_centroidW1 += w; d3_geo_centroidX1 += w * (x0 + (x0 = x)); d3_geo_centroidY1 += w * (y0 + (y0 = y)); d3_geo_centroidZ1 += w * (z0 + (z0 = z)); d3_geo_centroidPointXYZ(x0, y0, z0); } } function d3_geo_centroidLineEnd() { d3_geo_centroid.point = d3_geo_centroidPoint; } function d3_geo_centroidRingStart() { var λ00, φ00, x0, y0, z0; d3_geo_centroid.point = function(λ, φ) { λ00 = λ, φ00 = φ; d3_geo_centroid.point = nextPoint; λ *= d3_radians; var cosφ = Math.cos(φ *= d3_radians); x0 = cosφ * Math.cos(λ); y0 = cosφ * Math.sin(λ); z0 = Math.sin(φ); d3_geo_centroidPointXYZ(x0, y0, z0); }; d3_geo_centroid.lineEnd = function() { nextPoint(λ00, φ00); d3_geo_centroid.lineEnd = d3_geo_centroidLineEnd; d3_geo_centroid.point = d3_geo_centroidPoint; }; function nextPoint(λ, φ) { λ *= d3_radians; var cosφ = Math.cos(φ *= d3_radians), x = cosφ * Math.cos(λ), y = cosφ * Math.sin(λ), z = Math.sin(φ), cx = y0 * z - z0 * y, cy = z0 * x - x0 * z, cz = x0 * y - y0 * x, m = Math.sqrt(cx * cx + cy * cy + cz * cz), u = x0 * x + y0 * y + z0 * z, v = m && -d3_acos(u) / m, w = Math.atan2(m, u); d3_geo_centroidX2 += v * cx; d3_geo_centroidY2 += v * cy; d3_geo_centroidZ2 += v * cz; d3_geo_centroidW1 += w; d3_geo_centroidX1 += w * (x0 + (x0 = x)); d3_geo_centroidY1 += w * (y0 + (y0 = y)); d3_geo_centroidZ1 += w * (z0 + (z0 = z)); d3_geo_centroidPointXYZ(x0, y0, z0); } } function d3_geo_compose(a, b) { function compose(x, y) { return x = a(x, y), b(x[0], x[1]); } if (a.invert && b.invert) compose.invert = function(x, y) { return x = b.invert(x, y), x && a.invert(x[0], x[1]); }; return compose; } function d3_true() { return true; } function d3_geo_clipPolygon(segments, compare, clipStartInside, interpolate, listener) { var subject = [], clip = []; segments.forEach(function(segment) { if ((n = segment.length - 1) <= 0) return; var n, p0 = segment[0], p1 = segment[n]; if (d3_geo_sphericalEqual(p0, p1)) { listener.lineStart(); for (var i = 0; i < n; ++i) listener.point((p0 = segment[i])[0], p0[1]); listener.lineEnd(); return; } var a = new d3_geo_clipPolygonIntersection(p0, segment, null, true), b = new d3_geo_clipPolygonIntersection(p0, null, a, false); a.o = b; subject.push(a); clip.push(b); a = new d3_geo_clipPolygonIntersection(p1, segment, null, false); b = new d3_geo_clipPolygonIntersection(p1, null, a, true); a.o = b; subject.push(a); clip.push(b); }); clip.sort(compare); d3_geo_clipPolygonLinkCircular(subject); d3_geo_clipPolygonLinkCircular(clip); if (!subject.length) return; for (var i = 0, entry = clipStartInside, n = clip.length; i < n; ++i) { clip[i].e = entry = !entry; } var start = subject[0], points, point; while (1) { var current = start, isSubject = true; while (current.v) if ((current = current.n) === start) return; points = current.z; listener.lineStart(); do { current.v = current.o.v = true; if (current.e) { if (isSubject) { for (var i = 0, n = points.length; i < n; ++i) listener.point((point = points[i])[0], point[1]); } else { interpolate(current.x, current.n.x, 1, listener); } current = current.n; } else { if (isSubject) { points = current.p.z; for (var i = points.length - 1; i >= 0; --i) listener.point((point = points[i])[0], point[1]); } else { interpolate(current.x, current.p.x, -1, listener); } current = current.p; } current = current.o; points = current.z; isSubject = !isSubject; } while (!current.v); listener.lineEnd(); } } function d3_geo_clipPolygonLinkCircular(array) { if (!(n = array.length)) return; var n, i = 0, a = array[0], b; while (++i < n) { a.n = b = array[i]; b.p = a; a = b; } a.n = b = array[0]; b.p = a; } function d3_geo_clipPolygonIntersection(point, points, other, entry) { this.x = point; this.z = points; this.o = other; this.e = entry; this.v = false; this.n = this.p = null; } function d3_geo_clip(pointVisible, clipLine, interpolate, clipStart) { return function(rotate, listener) { var line = clipLine(listener), rotatedClipStart = rotate.invert(clipStart[0], clipStart[1]); var clip = { point: point, lineStart: lineStart, lineEnd: lineEnd, polygonStart: function() { clip.point = pointRing; clip.lineStart = ringStart; clip.lineEnd = ringEnd; segments = []; polygon = []; }, polygonEnd: function() { clip.point = point; clip.lineStart = lineStart; clip.lineEnd = lineEnd; segments = d3.merge(segments); var clipStartInside = d3_geo_pointInPolygon(rotatedClipStart, polygon); if (segments.length) { if (!polygonStarted) listener.polygonStart(), polygonStarted = true; d3_geo_clipPolygon(segments, d3_geo_clipSort, clipStartInside, interpolate, listener); } else if (clipStartInside) { if (!polygonStarted) listener.polygonStart(), polygonStarted = true; listener.lineStart(); interpolate(null, null, 1, listener); listener.lineEnd(); } if (polygonStarted) listener.polygonEnd(), polygonStarted = false; segments = polygon = null; }, sphere: function() { listener.polygonStart(); listener.lineStart(); interpolate(null, null, 1, listener); listener.lineEnd(); listener.polygonEnd(); } }; function point(λ, φ) { var point = rotate(λ, φ); if (pointVisible(λ = point[0], φ = point[1])) listener.point(λ, φ); } function pointLine(λ, φ) { var point = rotate(λ, φ); line.point(point[0], point[1]); } function lineStart() { clip.point = pointLine; line.lineStart(); } function lineEnd() { clip.point = point; line.lineEnd(); } var segments; var buffer = d3_geo_clipBufferListener(), ringListener = clipLine(buffer), polygonStarted = false, polygon, ring; function pointRing(λ, φ) { ring.push([ λ, φ ]); var point = rotate(λ, φ); ringListener.point(point[0], point[1]); } function ringStart() { ringListener.lineStart(); ring = []; } function ringEnd() { pointRing(ring[0][0], ring[0][1]); ringListener.lineEnd(); var clean = ringListener.clean(), ringSegments = buffer.buffer(), segment, n = ringSegments.length; ring.pop(); polygon.push(ring); ring = null; if (!n) return; if (clean & 1) { segment = ringSegments[0]; var n = segment.length - 1, i = -1, point; if (n > 0) { if (!polygonStarted) listener.polygonStart(), polygonStarted = true; listener.lineStart(); while (++i < n) listener.point((point = segment[i])[0], point[1]); listener.lineEnd(); } return; } if (n > 1 && clean & 2) ringSegments.push(ringSegments.pop().concat(ringSegments.shift())); segments.push(ringSegments.filter(d3_geo_clipSegmentLength1)); } return clip; }; } function d3_geo_clipSegmentLength1(segment) { return segment.length > 1; } function d3_geo_clipBufferListener() { var lines = [], line; return { lineStart: function() { lines.push(line = []); }, point: function(λ, φ) { line.push([ λ, φ ]); }, lineEnd: d3_noop, buffer: function() { var buffer = lines; lines = []; line = null; return buffer; }, rejoin: function() { if (lines.length > 1) lines.push(lines.pop().concat(lines.shift())); } }; } function d3_geo_clipSort(a, b) { return ((a = a.x)[0] < 0 ? a[1] - halfπ - ε : halfπ - a[1]) - ((b = b.x)[0] < 0 ? b[1] - halfπ - ε : halfπ - b[1]); } var d3_geo_clipAntimeridian = d3_geo_clip(d3_true, d3_geo_clipAntimeridianLine, d3_geo_clipAntimeridianInterpolate, [ -π, -π / 2 ]); function d3_geo_clipAntimeridianLine(listener) { var λ0 = NaN, φ0 = NaN, sλ0 = NaN, clean; return { lineStart: function() { listener.lineStart(); clean = 1; }, point: function(λ1, φ1) { var sλ1 = λ1 > 0 ? π : -π, dλ = abs(λ1 - λ0); if (abs(dλ - π) < ε) { listener.point(λ0, φ0 = (φ0 + φ1) / 2 > 0 ? halfπ : -halfπ); listener.point(sλ0, φ0); listener.lineEnd(); listener.lineStart(); listener.point(sλ1, φ0); listener.point(λ1, φ0); clean = 0; } else if (sλ0 !== sλ1 && dλ >= π) { if (abs(λ0 - sλ0) < ε) λ0 -= sλ0 * ε; if (abs(λ1 - sλ1) < ε) λ1 -= sλ1 * ε; φ0 = d3_geo_clipAntimeridianIntersect(λ0, φ0, λ1, φ1); listener.point(sλ0, φ0); listener.lineEnd(); listener.lineStart(); listener.point(sλ1, φ0); clean = 0; } listener.point(λ0 = λ1, φ0 = φ1); sλ0 = sλ1; }, lineEnd: function() { listener.lineEnd(); λ0 = φ0 = NaN; }, clean: function() { return 2 - clean; } }; } function d3_geo_clipAntimeridianIntersect(λ0, φ0, λ1, φ1) { var cosφ0, cosφ1, sinλ0_λ1 = Math.sin(λ0 - λ1); return abs(sinλ0_λ1) > ε ? Math.atan((Math.sin(φ0) * (cosφ1 = Math.cos(φ1)) * Math.sin(λ1) - Math.sin(φ1) * (cosφ0 = Math.cos(φ0)) * Math.sin(λ0)) / (cosφ0 * cosφ1 * sinλ0_λ1)) : (φ0 + φ1) / 2; } function d3_geo_clipAntimeridianInterpolate(from, to, direction, listener) { var φ; if (from == null) { φ = direction * halfπ; listener.point(-π, φ); listener.point(0, φ); listener.point(π, φ); listener.point(π, 0); listener.point(π, -φ); listener.point(0, -φ); listener.point(-π, -φ); listener.point(-π, 0); listener.point(-π, φ); } else if (abs(from[0] - to[0]) > ε) { var s = from[0] < to[0] ? π : -π; φ = direction * s / 2; listener.point(-s, φ); listener.point(0, φ); listener.point(s, φ); } else { listener.point(to[0], to[1]); } } function d3_geo_pointInPolygon(point, polygon) { var meridian = point[0], parallel = point[1], meridianNormal = [ Math.sin(meridian), -Math.cos(meridian), 0 ], polarAngle = 0, winding = 0; d3_geo_areaRingSum.reset(); for (var i = 0, n = polygon.length; i < n; ++i) { var ring = polygon[i], m = ring.length; if (!m) continue; var point0 = ring[0], λ0 = point0[0], φ0 = point0[1] / 2 + π / 4, sinφ0 = Math.sin(φ0), cosφ0 = Math.cos(φ0), j = 1; while (true) { if (j === m) j = 0; point = ring[j]; var λ = point[0], φ = point[1] / 2 + π / 4, sinφ = Math.sin(φ), cosφ = Math.cos(φ), dλ = λ - λ0, sdλ = dλ >= 0 ? 1 : -1, adλ = sdλ * dλ, antimeridian = adλ > π, k = sinφ0 * sinφ; d3_geo_areaRingSum.add(Math.atan2(k * sdλ * Math.sin(adλ), cosφ0 * cosφ + k * Math.cos(adλ))); polarAngle += antimeridian ? dλ + sdλ * τ : dλ; if (antimeridian ^ λ0 >= meridian ^ λ >= meridian) { var arc = d3_geo_cartesianCross(d3_geo_cartesian(point0), d3_geo_cartesian(point)); d3_geo_cartesianNormalize(arc); var intersection = d3_geo_cartesianCross(meridianNormal, arc); d3_geo_cartesianNormalize(intersection); var φarc = (antimeridian ^ dλ >= 0 ? -1 : 1) * d3_asin(intersection[2]); if (parallel > φarc || parallel === φarc && (arc[0] || arc[1])) { winding += antimeridian ^ dλ >= 0 ? 1 : -1; } } if (!j++) break; λ0 = λ, sinφ0 = sinφ, cosφ0 = cosφ, point0 = point; } } return (polarAngle < -ε || polarAngle < ε && d3_geo_areaRingSum < -ε) ^ winding & 1; } function d3_geo_clipCircle(radius) { var cr = Math.cos(radius), smallRadius = cr > 0, notHemisphere = abs(cr) > ε, interpolate = d3_geo_circleInterpolate(radius, 6 * d3_radians); return d3_geo_clip(visible, clipLine, interpolate, smallRadius ? [ 0, -radius ] : [ -π, radius - π ]); function visible(λ, φ) { return Math.cos(λ) * Math.cos(φ) > cr; } function clipLine(listener) { var point0, c0, v0, v00, clean; return { lineStart: function() { v00 = v0 = false; clean = 1; }, point: function(λ, φ) { var point1 = [ λ, φ ], point2, v = visible(λ, φ), c = smallRadius ? v ? 0 : code(λ, φ) : v ? code(λ + (λ < 0 ? π : -π), φ) : 0; if (!point0 && (v00 = v0 = v)) listener.lineStart(); if (v !== v0) { point2 = intersect(point0, point1); if (d3_geo_sphericalEqual(point0, point2) || d3_geo_sphericalEqual(point1, point2)) { point1[0] += ε; point1[1] += ε; v = visible(point1[0], point1[1]); } } if (v !== v0) { clean = 0; if (v) { listener.lineStart(); point2 = intersect(point1, point0); listener.point(point2[0], point2[1]); } else { point2 = intersect(point0, point1); listener.point(point2[0], point2[1]); listener.lineEnd(); } point0 = point2; } else if (notHemisphere && point0 && smallRadius ^ v) { var t; if (!(c & c0) && (t = intersect(point1, point0, true))) { clean = 0; if (smallRadius) { listener.lineStart(); listener.point(t[0][0], t[0][1]); listener.point(t[1][0], t[1][1]); listener.lineEnd(); } else { listener.point(t[1][0], t[1][1]); listener.lineEnd(); listener.lineStart(); listener.point(t[0][0], t[0][1]); } } } if (v && (!point0 || !d3_geo_sphericalEqual(point0, point1))) { listener.point(point1[0], point1[1]); } point0 = point1, v0 = v, c0 = c; }, lineEnd: function() { if (v0) listener.lineEnd(); point0 = null; }, clean: function() { return clean | (v00 && v0) << 1; } }; } function intersect(a, b, two) { var pa = d3_geo_cartesian(a), pb = d3_geo_cartesian(b); var n1 = [ 1, 0, 0 ], n2 = d3_geo_cartesianCross(pa, pb), n2n2 = d3_geo_cartesianDot(n2, n2), n1n2 = n2[0], determinant = n2n2 - n1n2 * n1n2; if (!determinant) return !two && a; var c1 = cr * n2n2 / determinant, c2 = -cr * n1n2 / determinant, n1xn2 = d3_geo_cartesianCross(n1, n2), A = d3_geo_cartesianScale(n1, c1), B = d3_geo_cartesianScale(n2, c2); d3_geo_cartesianAdd(A, B); var u = n1xn2, w = d3_geo_cartesianDot(A, u), uu = d3_geo_cartesianDot(u, u), t2 = w * w - uu * (d3_geo_cartesianDot(A, A) - 1); if (t2 < 0) return; var t = Math.sqrt(t2), q = d3_geo_cartesianScale(u, (-w - t) / uu); d3_geo_cartesianAdd(q, A); q = d3_geo_spherical(q); if (!two) return q; var λ0 = a[0], λ1 = b[0], φ0 = a[1], φ1 = b[1], z; if (λ1 < λ0) z = λ0, λ0 = λ1, λ1 = z; var δλ = λ1 - λ0, polar = abs(δλ - π) < ε, meridian = polar || δλ < ε; if (!polar && φ1 < φ0) z = φ0, φ0 = φ1, φ1 = z; if (meridian ? polar ? φ0 + φ1 > 0 ^ q[1] < (abs(q[0] - λ0) < ε ? φ0 : φ1) : φ0 <= q[1] && q[1] <= φ1 : δλ > π ^ (λ0 <= q[0] && q[0] <= λ1)) { var q1 = d3_geo_cartesianScale(u, (-w + t) / uu); d3_geo_cartesianAdd(q1, A); return [ q, d3_geo_spherical(q1) ]; } } function code(λ, φ) { var r = smallRadius ? radius : π - radius, code = 0; if (λ < -r) code |= 1; else if (λ > r) code |= 2; if (φ < -r) code |= 4; else if (φ > r) code |= 8; return code; } } function d3_geom_clipLine(x0, y0, x1, y1) { return function(line) { var a = line.a, b = line.b, ax = a.x, ay = a.y, bx = b.x, by = b.y, t0 = 0, t1 = 1, dx = bx - ax, dy = by - ay, r; r = x0 - ax; if (!dx && r > 0) return; r /= dx; if (dx < 0) { if (r < t0) return; if (r < t1) t1 = r; } else if (dx > 0) { if (r > t1) return; if (r > t0) t0 = r; } r = x1 - ax; if (!dx && r < 0) return; r /= dx; if (dx < 0) { if (r > t1) return; if (r > t0) t0 = r; } else if (dx > 0) { if (r < t0) return; if (r < t1) t1 = r; } r = y0 - ay; if (!dy && r > 0) return; r /= dy; if (dy < 0) { if (r < t0) return; if (r < t1) t1 = r; } else if (dy > 0) { if (r > t1) return; if (r > t0) t0 = r; } r = y1 - ay; if (!dy && r < 0) return; r /= dy; if (dy < 0) { if (r > t1) return; if (r > t0) t0 = r; } else if (dy > 0) { if (r < t0) return; if (r < t1) t1 = r; } if (t0 > 0) line.a = { x: ax + t0 * dx, y: ay + t0 * dy }; if (t1 < 1) line.b = { x: ax + t1 * dx, y: ay + t1 * dy }; return line; }; } var d3_geo_clipExtentMAX = 1e9; d3.geo.clipExtent = function() { var x0, y0, x1, y1, stream, clip, clipExtent = { stream: function(output) { if (stream) stream.valid = false; stream = clip(output); stream.valid = true; return stream; }, extent: function(_) { if (!arguments.length) return [ [ x0, y0 ], [ x1, y1 ] ]; clip = d3_geo_clipExtent(x0 = +_[0][0], y0 = +_[0][1], x1 = +_[1][0], y1 = +_[1][1]); if (stream) stream.valid = false, stream = null; return clipExtent; } }; return clipExtent.extent([ [ 0, 0 ], [ 960, 500 ] ]); }; function d3_geo_clipExtent(x0, y0, x1, y1) { return function(listener) { var listener_ = listener, bufferListener = d3_geo_clipBufferListener(), clipLine = d3_geom_clipLine(x0, y0, x1, y1), segments, polygon, ring; var clip = { point: point, lineStart: lineStart, lineEnd: lineEnd, polygonStart: function() { listener = bufferListener; segments = []; polygon = []; clean = true; }, polygonEnd: function() { listener = listener_; segments = d3.merge(segments); var clipStartInside = insidePolygon([ x0, y1 ]), inside = clean && clipStartInside, visible = segments.length; if (inside || visible) { listener.polygonStart(); if (inside) { listener.lineStart(); interpolate(null, null, 1, listener); listener.lineEnd(); } if (visible) { d3_geo_clipPolygon(segments, compare, clipStartInside, interpolate, listener); } listener.polygonEnd(); } segments = polygon = ring = null; } }; function insidePolygon(p) { var wn = 0, n = polygon.length, y = p[1]; for (var i = 0; i < n; ++i) { for (var j = 1, v = polygon[i], m = v.length, a = v[0], b; j < m; ++j) { b = v[j]; if (a[1] <= y) { if (b[1] > y && d3_cross2d(a, b, p) > 0) ++wn; } else { if (b[1] <= y && d3_cross2d(a, b, p) < 0) --wn; } a = b; } } return wn !== 0; } function interpolate(from, to, direction, listener) { var a = 0, a1 = 0; if (from == null || (a = corner(from, direction)) !== (a1 = corner(to, direction)) || comparePoints(from, to) < 0 ^ direction > 0) { do { listener.point(a === 0 || a === 3 ? x0 : x1, a > 1 ? y1 : y0); } while ((a = (a + direction + 4) % 4) !== a1); } else { listener.point(to[0], to[1]); } } function pointVisible(x, y) { return x0 <= x && x <= x1 && y0 <= y && y <= y1; } function point(x, y) { if (pointVisible(x, y)) listener.point(x, y); } var x__, y__, v__, x_, y_, v_, first, clean; function lineStart() { clip.point = linePoint; if (polygon) polygon.push(ring = []); first = true; v_ = false; x_ = y_ = NaN; } function lineEnd() { if (segments) { linePoint(x__, y__); if (v__ && v_) bufferListener.rejoin(); segments.push(bufferListener.buffer()); } clip.point = point; if (v_) listener.lineEnd(); } function linePoint(x, y) { x = Math.max(-d3_geo_clipExtentMAX, Math.min(d3_geo_clipExtentMAX, x)); y = Math.max(-d3_geo_clipExtentMAX, Math.min(d3_geo_clipExtentMAX, y)); var v = pointVisible(x, y); if (polygon) ring.push([ x, y ]); if (first) { x__ = x, y__ = y, v__ = v; first = false; if (v) { listener.lineStart(); listener.point(x, y); } } else { if (v && v_) listener.point(x, y); else { var l = { a: { x: x_, y: y_ }, b: { x: x, y: y } }; if (clipLine(l)) { if (!v_) { listener.lineStart(); listener.point(l.a.x, l.a.y); } listener.point(l.b.x, l.b.y); if (!v) listener.lineEnd(); clean = false; } else if (v) { listener.lineStart(); listener.point(x, y); clean = false; } } } x_ = x, y_ = y, v_ = v; } return clip; }; function corner(p, direction) { return abs(p[0] - x0) < ε ? direction > 0 ? 0 : 3 : abs(p[0] - x1) < ε ? direction > 0 ? 2 : 1 : abs(p[1] - y0) < ε ? direction > 0 ? 1 : 0 : direction > 0 ? 3 : 2; } function compare(a, b) { return comparePoints(a.x, b.x); } function comparePoints(a, b) { var ca = corner(a, 1), cb = corner(b, 1); return ca !== cb ? ca - cb : ca === 0 ? b[1] - a[1] : ca === 1 ? a[0] - b[0] : ca === 2 ? a[1] - b[1] : b[0] - a[0]; } } function d3_geo_conic(projectAt) { var φ0 = 0, φ1 = π / 3, m = d3_geo_projectionMutator(projectAt), p = m(φ0, φ1); p.parallels = function(_) { if (!arguments.length) return [ φ0 / π * 180, φ1 / π * 180 ]; return m(φ0 = _[0] * π / 180, φ1 = _[1] * π / 180); }; return p; } function d3_geo_conicEqualArea(φ0, φ1) { var sinφ0 = Math.sin(φ0), n = (sinφ0 + Math.sin(φ1)) / 2, C = 1 + sinφ0 * (2 * n - sinφ0), ρ0 = Math.sqrt(C) / n; function forward(λ, φ) { var ρ = Math.sqrt(C - 2 * n * Math.sin(φ)) / n; return [ ρ * Math.sin(λ *= n), ρ0 - ρ * Math.cos(λ) ]; } forward.invert = function(x, y) { var ρ0_y = ρ0 - y; return [ Math.atan2(x, ρ0_y) / n, d3_asin((C - (x * x + ρ0_y * ρ0_y) * n * n) / (2 * n)) ]; }; return forward; } (d3.geo.conicEqualArea = function() { return d3_geo_conic(d3_geo_conicEqualArea); }).raw = d3_geo_conicEqualArea; d3.geo.albers = function() { return d3.geo.conicEqualArea().rotate([ 96, 0 ]).center([ -.6, 38.7 ]).parallels([ 29.5, 45.5 ]).scale(1070); }; d3.geo.albersUsa = function() { var lower48 = d3.geo.albers(); var alaska = d3.geo.conicEqualArea().rotate([ 154, 0 ]).center([ -2, 58.5 ]).parallels([ 55, 65 ]); var hawaii = d3.geo.conicEqualArea().rotate([ 157, 0 ]).center([ -3, 19.9 ]).parallels([ 8, 18 ]); var point, pointStream = { point: function(x, y) { point = [ x, y ]; } }, lower48Point, alaskaPoint, hawaiiPoint; function albersUsa(coordinates) { var x = coordinates[0], y = coordinates[1]; point = null; (lower48Point(x, y), point) || (alaskaPoint(x, y), point) || hawaiiPoint(x, y); return point; } albersUsa.invert = function(coordinates) { var k = lower48.scale(), t = lower48.translate(), x = (coordinates[0] - t[0]) / k, y = (coordinates[1] - t[1]) / k; return (y >= .12 && y < .234 && x >= -.425 && x < -.214 ? alaska : y >= .166 && y < .234 && x >= -.214 && x < -.115 ? hawaii : lower48).invert(coordinates); }; albersUsa.stream = function(stream) { var lower48Stream = lower48.stream(stream), alaskaStream = alaska.stream(stream), hawaiiStream = hawaii.stream(stream); return { point: function(x, y) { lower48Stream.point(x, y); alaskaStream.point(x, y); hawaiiStream.point(x, y); }, sphere: function() { lower48Stream.sphere(); alaskaStream.sphere(); hawaiiStream.sphere(); }, lineStart: function() { lower48Stream.lineStart(); alaskaStream.lineStart(); hawaiiStream.lineStart(); }, lineEnd: function() { lower48Stream.lineEnd(); alaskaStream.lineEnd(); hawaiiStream.lineEnd(); }, polygonStart: function() { lower48Stream.polygonStart(); alaskaStream.polygonStart(); hawaiiStream.polygonStart(); }, polygonEnd: function() { lower48Stream.polygonEnd(); alaskaStream.polygonEnd(); hawaiiStream.polygonEnd(); } }; }; albersUsa.precision = function(_) { if (!arguments.length) return lower48.precision(); lower48.precision(_); alaska.precision(_); hawaii.precision(_); return albersUsa; }; albersUsa.scale = function(_) { if (!arguments.length) return lower48.scale(); lower48.scale(_); alaska.scale(_ * .35); hawaii.scale(_); return albersUsa.translate(lower48.translate()); }; albersUsa.translate = function(_) { if (!arguments.length) return lower48.translate(); var k = lower48.scale(), x = +_[0], y = +_[1]; lower48Point = lower48.translate(_).clipExtent([ [ x - .455 * k, y - .238 * k ], [ x + .455 * k, y + .238 * k ] ]).stream(pointStream).point; alaskaPoint = alaska.translate([ x - .307 * k, y + .201 * k ]).clipExtent([ [ x - .425 * k + ε, y + .12 * k + ε ], [ x - .214 * k - ε, y + .234 * k - ε ] ]).stream(pointStream).point; hawaiiPoint = hawaii.translate([ x - .205 * k, y + .212 * k ]).clipExtent([ [ x - .214 * k + ε, y + .166 * k + ε ], [ x - .115 * k - ε, y + .234 * k - ε ] ]).stream(pointStream).point; return albersUsa; }; return albersUsa.scale(1070); }; var d3_geo_pathAreaSum, d3_geo_pathAreaPolygon, d3_geo_pathArea = { point: d3_noop, lineStart: d3_noop, lineEnd: d3_noop, polygonStart: function() { d3_geo_pathAreaPolygon = 0; d3_geo_pathArea.lineStart = d3_geo_pathAreaRingStart; }, polygonEnd: function() { d3_geo_pathArea.lineStart = d3_geo_pathArea.lineEnd = d3_geo_pathArea.point = d3_noop; d3_geo_pathAreaSum += abs(d3_geo_pathAreaPolygon / 2); } }; function d3_geo_pathAreaRingStart() { var x00, y00, x0, y0; d3_geo_pathArea.point = function(x, y) { d3_geo_pathArea.point = nextPoint; x00 = x0 = x, y00 = y0 = y; }; function nextPoint(x, y) { d3_geo_pathAreaPolygon += y0 * x - x0 * y; x0 = x, y0 = y; } d3_geo_pathArea.lineEnd = function() { nextPoint(x00, y00); }; } var d3_geo_pathBoundsX0, d3_geo_pathBoundsY0, d3_geo_pathBoundsX1, d3_geo_pathBoundsY1; var d3_geo_pathBounds = { point: d3_geo_pathBoundsPoint, lineStart: d3_noop, lineEnd: d3_noop, polygonStart: d3_noop, polygonEnd: d3_noop }; function d3_geo_pathBoundsPoint(x, y) { if (x < d3_geo_pathBoundsX0) d3_geo_pathBoundsX0 = x; if (x > d3_geo_pathBoundsX1) d3_geo_pathBoundsX1 = x; if (y < d3_geo_pathBoundsY0) d3_geo_pathBoundsY0 = y; if (y > d3_geo_pathBoundsY1) d3_geo_pathBoundsY1 = y; } function d3_geo_pathBuffer() { var pointCircle = d3_geo_pathBufferCircle(4.5), buffer = []; var stream = { point: point, lineStart: function() { stream.point = pointLineStart; }, lineEnd: lineEnd, polygonStart: function() { stream.lineEnd = lineEndPolygon; }, polygonEnd: function() { stream.lineEnd = lineEnd; stream.point = point; }, pointRadius: function(_) { pointCircle = d3_geo_pathBufferCircle(_); return stream; }, result: function() { if (buffer.length) { var result = buffer.join(""); buffer = []; return result; } } }; function point(x, y) { buffer.push("M", x, ",", y, pointCircle); } function pointLineStart(x, y) { buffer.push("M", x, ",", y); stream.point = pointLine; } function pointLine(x, y) { buffer.push("L", x, ",", y); } function lineEnd() { stream.point = point; } function lineEndPolygon() { buffer.push("Z"); } return stream; } function d3_geo_pathBufferCircle(radius) { return "m0," + radius + "a" + radius + "," + radius + " 0 1,1 0," + -2 * radius + "a" + radius + "," + radius + " 0 1,1 0," + 2 * radius + "z"; } var d3_geo_pathCentroid = { point: d3_geo_pathCentroidPoint, lineStart: d3_geo_pathCentroidLineStart, lineEnd: d3_geo_pathCentroidLineEnd, polygonStart: function() { d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidRingStart; }, polygonEnd: function() { d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint; d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidLineStart; d3_geo_pathCentroid.lineEnd = d3_geo_pathCentroidLineEnd; } }; function d3_geo_pathCentroidPoint(x, y) { d3_geo_centroidX0 += x; d3_geo_centroidY0 += y; ++d3_geo_centroidZ0; } function d3_geo_pathCentroidLineStart() { var x0, y0; d3_geo_pathCentroid.point = function(x, y) { d3_geo_pathCentroid.point = nextPoint; d3_geo_pathCentroidPoint(x0 = x, y0 = y); }; function nextPoint(x, y) { var dx = x - x0, dy = y - y0, z = Math.sqrt(dx * dx + dy * dy); d3_geo_centroidX1 += z * (x0 + x) / 2; d3_geo_centroidY1 += z * (y0 + y) / 2; d3_geo_centroidZ1 += z; d3_geo_pathCentroidPoint(x0 = x, y0 = y); } } function d3_geo_pathCentroidLineEnd() { d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint; } function d3_geo_pathCentroidRingStart() { var x00, y00, x0, y0; d3_geo_pathCentroid.point = function(x, y) { d3_geo_pathCentroid.point = nextPoint; d3_geo_pathCentroidPoint(x00 = x0 = x, y00 = y0 = y); }; function nextPoint(x, y) { var dx = x - x0, dy = y - y0, z = Math.sqrt(dx * dx + dy * dy); d3_geo_centroidX1 += z * (x0 + x) / 2; d3_geo_centroidY1 += z * (y0 + y) / 2; d3_geo_centroidZ1 += z; z = y0 * x - x0 * y; d3_geo_centroidX2 += z * (x0 + x); d3_geo_centroidY2 += z * (y0 + y); d3_geo_centroidZ2 += z * 3; d3_geo_pathCentroidPoint(x0 = x, y0 = y); } d3_geo_pathCentroid.lineEnd = function() { nextPoint(x00, y00); }; } function d3_geo_pathContext(context) { var pointRadius = 4.5; var stream = { point: point, lineStart: function() { stream.point = pointLineStart; }, lineEnd: lineEnd, polygonStart: function() { stream.lineEnd = lineEndPolygon; }, polygonEnd: function() { stream.lineEnd = lineEnd; stream.point = point; }, pointRadius: function(_) { pointRadius = _; return stream; }, result: d3_noop }; function point(x, y) { context.moveTo(x + pointRadius, y); context.arc(x, y, pointRadius, 0, τ); } function pointLineStart(x, y) { context.moveTo(x, y); stream.point = pointLine; } function pointLine(x, y) { context.lineTo(x, y); } function lineEnd() { stream.point = point; } function lineEndPolygon() { context.closePath(); } return stream; } function d3_geo_resample(project) { var δ2 = .5, cosMinDistance = Math.cos(30 * d3_radians), maxDepth = 16; function resample(stream) { return (maxDepth ? resampleRecursive : resampleNone)(stream); } function resampleNone(stream) { return d3_geo_transformPoint(stream, function(x, y) { x = project(x, y); stream.point(x[0], x[1]); }); } function resampleRecursive(stream) { var λ00, φ00, x00, y00, a00, b00, c00, λ0, x0, y0, a0, b0, c0; var resample = { point: point, lineStart: lineStart, lineEnd: lineEnd, polygonStart: function() { stream.polygonStart(); resample.lineStart = ringStart; }, polygonEnd: function() { stream.polygonEnd(); resample.lineStart = lineStart; } }; function point(x, y) { x = project(x, y); stream.point(x[0], x[1]); } function lineStart() { x0 = NaN; resample.point = linePoint; stream.lineStart(); } function linePoint(λ, φ) { var c = d3_geo_cartesian([ λ, φ ]), p = project(λ, φ); resampleLineTo(x0, y0, λ0, a0, b0, c0, x0 = p[0], y0 = p[1], λ0 = λ, a0 = c[0], b0 = c[1], c0 = c[2], maxDepth, stream); stream.point(x0, y0); } function lineEnd() { resample.point = point; stream.lineEnd(); } function ringStart() { lineStart(); resample.point = ringPoint; resample.lineEnd = ringEnd; } function ringPoint(λ, φ) { linePoint(λ00 = λ, φ00 = φ), x00 = x0, y00 = y0, a00 = a0, b00 = b0, c00 = c0; resample.point = linePoint; } function ringEnd() { resampleLineTo(x0, y0, λ0, a0, b0, c0, x00, y00, λ00, a00, b00, c00, maxDepth, stream); resample.lineEnd = lineEnd; lineEnd(); } return resample; } function resampleLineTo(x0, y0, λ0, a0, b0, c0, x1, y1, λ1, a1, b1, c1, depth, stream) { var dx = x1 - x0, dy = y1 - y0, d2 = dx * dx + dy * dy; if (d2 > 4 * δ2 && depth--) { var a = a0 + a1, b = b0 + b1, c = c0 + c1, m = Math.sqrt(a * a + b * b + c * c), φ2 = Math.asin(c /= m), λ2 = abs(abs(c) - 1) < ε || abs(λ0 - λ1) < ε ? (λ0 + λ1) / 2 : Math.atan2(b, a), p = project(λ2, φ2), x2 = p[0], y2 = p[1], dx2 = x2 - x0, dy2 = y2 - y0, dz = dy * dx2 - dx * dy2; if (dz * dz / d2 > δ2 || abs((dx * dx2 + dy * dy2) / d2 - .5) > .3 || a0 * a1 + b0 * b1 + c0 * c1 < cosMinDistance) { resampleLineTo(x0, y0, λ0, a0, b0, c0, x2, y2, λ2, a /= m, b /= m, c, depth, stream); stream.point(x2, y2); resampleLineTo(x2, y2, λ2, a, b, c, x1, y1, λ1, a1, b1, c1, depth, stream); } } } resample.precision = function(_) { if (!arguments.length) return Math.sqrt(δ2); maxDepth = (δ2 = _ * _) > 0 && 16; return resample; }; return resample; } d3.geo.path = function() { var pointRadius = 4.5, projection, context, projectStream, contextStream, cacheStream; function path(object) { if (object) { if (typeof pointRadius === "function") contextStream.pointRadius(+pointRadius.apply(this, arguments)); if (!cacheStream || !cacheStream.valid) cacheStream = projectStream(contextStream); d3.geo.stream(object, cacheStream); } return contextStream.result(); } path.area = function(object) { d3_geo_pathAreaSum = 0; d3.geo.stream(object, projectStream(d3_geo_pathArea)); return d3_geo_pathAreaSum; }; path.centroid = function(object) { d3_geo_centroidX0 = d3_geo_centroidY0 = d3_geo_centroidZ0 = d3_geo_centroidX1 = d3_geo_centroidY1 = d3_geo_centroidZ1 = d3_geo_centroidX2 = d3_geo_centroidY2 = d3_geo_centroidZ2 = 0; d3.geo.stream(object, projectStream(d3_geo_pathCentroid)); return d3_geo_centroidZ2 ? [ d3_geo_centroidX2 / d3_geo_centroidZ2, d3_geo_centroidY2 / d3_geo_centroidZ2 ] : d3_geo_centroidZ1 ? [ d3_geo_centroidX1 / d3_geo_centroidZ1, d3_geo_centroidY1 / d3_geo_centroidZ1 ] : d3_geo_centroidZ0 ? [ d3_geo_centroidX0 / d3_geo_centroidZ0, d3_geo_centroidY0 / d3_geo_centroidZ0 ] : [ NaN, NaN ]; }; path.bounds = function(object) { d3_geo_pathBoundsX1 = d3_geo_pathBoundsY1 = -(d3_geo_pathBoundsX0 = d3_geo_pathBoundsY0 = Infinity); d3.geo.stream(object, projectStream(d3_geo_pathBounds)); return [ [ d3_geo_pathBoundsX0, d3_geo_pathBoundsY0 ], [ d3_geo_pathBoundsX1, d3_geo_pathBoundsY1 ] ]; }; path.projection = function(_) { if (!arguments.length) return projection; projectStream = (projection = _) ? _.stream || d3_geo_pathProjectStream(_) : d3_identity; return reset(); }; path.context = function(_) { if (!arguments.length) return context; contextStream = (context = _) == null ? new d3_geo_pathBuffer() : new d3_geo_pathContext(_); if (typeof pointRadius !== "function") contextStream.pointRadius(pointRadius); return reset(); }; path.pointRadius = function(_) { if (!arguments.length) return pointRadius; pointRadius = typeof _ === "function" ? _ : (contextStream.pointRadius(+_), +_); return path; }; function reset() { cacheStream = null; return path; } return path.projection(d3.geo.albersUsa()).context(null); }; function d3_geo_pathProjectStream(project) { var resample = d3_geo_resample(function(x, y) { return project([ x * d3_degrees, y * d3_degrees ]); }); return function(stream) { return d3_geo_projectionRadians(resample(stream)); }; } d3.geo.transform = function(methods) { return { stream: function(stream) { var transform = new d3_geo_transform(stream); for (var k in methods) transform[k] = methods[k]; return transform; } }; }; function d3_geo_transform(stream) { this.stream = stream; } d3_geo_transform.prototype = { point: function(x, y) { this.stream.point(x, y); }, sphere: function() { this.stream.sphere(); }, lineStart: function() { this.stream.lineStart(); }, lineEnd: function() { this.stream.lineEnd(); }, polygonStart: function() { this.stream.polygonStart(); }, polygonEnd: function() { this.stream.polygonEnd(); } }; function d3_geo_transformPoint(stream, point) { return { point: point, sphere: function() { stream.sphere(); }, lineStart: function() { stream.lineStart(); }, lineEnd: function() { stream.lineEnd(); }, polygonStart: function() { stream.polygonStart(); }, polygonEnd: function() { stream.polygonEnd(); } }; } d3.geo.projection = d3_geo_projection; d3.geo.projectionMutator = d3_geo_projectionMutator; function d3_geo_projection(project) { return d3_geo_projectionMutator(function() { return project; })(); } function d3_geo_projectionMutator(projectAt) { var project, rotate, projectRotate, projectResample = d3_geo_resample(function(x, y) { x = project(x, y); return [ x[0] * k + δx, δy - x[1] * k ]; }), k = 150, x = 480, y = 250, λ = 0, φ = 0, δλ = 0, δφ = 0, δγ = 0, δx, δy, preclip = d3_geo_clipAntimeridian, postclip = d3_identity, clipAngle = null, clipExtent = null, stream; function projection(point) { point = projectRotate(point[0] * d3_radians, point[1] * d3_radians); return [ point[0] * k + δx, δy - point[1] * k ]; } function invert(point) { point = projectRotate.invert((point[0] - δx) / k, (δy - point[1]) / k); return point && [ point[0] * d3_degrees, point[1] * d3_degrees ]; } projection.stream = function(output) { if (stream) stream.valid = false; stream = d3_geo_projectionRadians(preclip(rotate, projectResample(postclip(output)))); stream.valid = true; return stream; }; projection.clipAngle = function(_) { if (!arguments.length) return clipAngle; preclip = _ == null ? (clipAngle = _, d3_geo_clipAntimeridian) : d3_geo_clipCircle((clipAngle = +_) * d3_radians); return invalidate(); }; projection.clipExtent = function(_) { if (!arguments.length) return clipExtent; clipExtent = _; postclip = _ ? d3_geo_clipExtent(_[0][0], _[0][1], _[1][0], _[1][1]) : d3_identity; return invalidate(); }; projection.scale = function(_) { if (!arguments.length) return k; k = +_; return reset(); }; projection.translate = function(_) { if (!arguments.length) return [ x, y ]; x = +_[0]; y = +_[1]; return reset(); }; projection.center = function(_) { if (!arguments.length) return [ λ * d3_degrees, φ * d3_degrees ]; λ = _[0] % 360 * d3_radians; φ = _[1] % 360 * d3_radians; return reset(); }; projection.rotate = function(_) { if (!arguments.length) return [ δλ * d3_degrees, δφ * d3_degrees, δγ * d3_degrees ]; δλ = _[0] % 360 * d3_radians; δφ = _[1] % 360 * d3_radians; δγ = _.length > 2 ? _[2] % 360 * d3_radians : 0; return reset(); }; d3.rebind(projection, projectResample, "precision"); function reset() { projectRotate = d3_geo_compose(rotate = d3_geo_rotation(δλ, δφ, δγ), project); var center = project(λ, φ); δx = x - center[0] * k; δy = y + center[1] * k; return invalidate(); } function invalidate() { if (stream) stream.valid = false, stream = null; return projection; } return function() { project = projectAt.apply(this, arguments); projection.invert = project.invert && invert; return reset(); }; } function d3_geo_projectionRadians(stream) { return d3_geo_transformPoint(stream, function(x, y) { stream.point(x * d3_radians, y * d3_radians); }); } function d3_geo_equirectangular(λ, φ) { return [ λ, φ ]; } (d3.geo.equirectangular = function() { return d3_geo_projection(d3_geo_equirectangular); }).raw = d3_geo_equirectangular.invert = d3_geo_equirectangular; d3.geo.rotation = function(rotate) { rotate = d3_geo_rotation(rotate[0] % 360 * d3_radians, rotate[1] * d3_radians, rotate.length > 2 ? rotate[2] * d3_radians : 0); function forward(coordinates) { coordinates = rotate(coordinates[0] * d3_radians, coordinates[1] * d3_radians); return coordinates[0] *= d3_degrees, coordinates[1] *= d3_degrees, coordinates; } forward.invert = function(coordinates) { coordinates = rotate.invert(coordinates[0] * d3_radians, coordinates[1] * d3_radians); return coordinates[0] *= d3_degrees, coordinates[1] *= d3_degrees, coordinates; }; return forward; }; function d3_geo_identityRotation(λ, φ) { return [ λ > π ? λ - τ : λ < -π ? λ + τ : λ, φ ]; } d3_geo_identityRotation.invert = d3_geo_equirectangular; function d3_geo_rotation(δλ, δφ, δγ) { return δλ ? δφ || δγ ? d3_geo_compose(d3_geo_rotationλ(δλ), d3_geo_rotationφγ(δφ, δγ)) : d3_geo_rotationλ(δλ) : δφ || δγ ? d3_geo_rotationφγ(δφ, δγ) : d3_geo_identityRotation; } function d3_geo_forwardRotationλ(δλ) { return function(λ, φ) { return λ += δλ, [ λ > π ? λ - τ : λ < -π ? λ + τ : λ, φ ]; }; } function d3_geo_rotationλ(δλ) { var rotation = d3_geo_forwardRotationλ(δλ); rotation.invert = d3_geo_forwardRotationλ(-δλ); return rotation; } function d3_geo_rotationφγ(δφ, δγ) { var cosδφ = Math.cos(δφ), sinδφ = Math.sin(δφ), cosδγ = Math.cos(δγ), sinδγ = Math.sin(δγ); function rotation(λ, φ) { var cosφ = Math.cos(φ), x = Math.cos(λ) * cosφ, y = Math.sin(λ) * cosφ, z = Math.sin(φ), k = z * cosδφ + x * sinδφ; return [ Math.atan2(y * cosδγ - k * sinδγ, x * cosδφ - z * sinδφ), d3_asin(k * cosδγ + y * sinδγ) ]; } rotation.invert = function(λ, φ) { var cosφ = Math.cos(φ), x = Math.cos(λ) * cosφ, y = Math.sin(λ) * cosφ, z = Math.sin(φ), k = z * cosδγ - y * sinδγ; return [ Math.atan2(y * cosδγ + z * sinδγ, x * cosδφ + k * sinδφ), d3_asin(k * cosδφ - x * sinδφ) ]; }; return rotation; } d3.geo.circle = function() { var origin = [ 0, 0 ], angle, precision = 6, interpolate; function circle() { var center = typeof origin === "function" ? origin.apply(this, arguments) : origin, rotate = d3_geo_rotation(-center[0] * d3_radians, -center[1] * d3_radians, 0).invert, ring = []; interpolate(null, null, 1, { point: function(x, y) { ring.push(x = rotate(x, y)); x[0] *= d3_degrees, x[1] *= d3_degrees; } }); return { type: "Polygon", coordinates: [ ring ] }; } circle.origin = function(x) { if (!arguments.length) return origin; origin = x; return circle; }; circle.angle = function(x) { if (!arguments.length) return angle; interpolate = d3_geo_circleInterpolate((angle = +x) * d3_radians, precision * d3_radians); return circle; }; circle.precision = function(_) { if (!arguments.length) return precision; interpolate = d3_geo_circleInterpolate(angle * d3_radians, (precision = +_) * d3_radians); return circle; }; return circle.angle(90); }; function d3_geo_circleInterpolate(radius, precision) { var cr = Math.cos(radius), sr = Math.sin(radius); return function(from, to, direction, listener) { var step = direction * precision; if (from != null) { from = d3_geo_circleAngle(cr, from); to = d3_geo_circleAngle(cr, to); if (direction > 0 ? from < to : from > to) from += direction * τ; } else { from = radius + direction * τ; to = radius - .5 * step; } for (var point, t = from; direction > 0 ? t > to : t < to; t -= step) { listener.point((point = d3_geo_spherical([ cr, -sr * Math.cos(t), -sr * Math.sin(t) ]))[0], point[1]); } }; } function d3_geo_circleAngle(cr, point) { var a = d3_geo_cartesian(point); a[0] -= cr; d3_geo_cartesianNormalize(a); var angle = d3_acos(-a[1]); return ((-a[2] < 0 ? -angle : angle) + 2 * Math.PI - ε) % (2 * Math.PI); } d3.geo.distance = function(a, b) { var Δλ = (b[0] - a[0]) * d3_radians, φ0 = a[1] * d3_radians, φ1 = b[1] * d3_radians, sinΔλ = Math.sin(Δλ), cosΔλ = Math.cos(Δλ), sinφ0 = Math.sin(φ0), cosφ0 = Math.cos(φ0), sinφ1 = Math.sin(φ1), cosφ1 = Math.cos(φ1), t; return Math.atan2(Math.sqrt((t = cosφ1 * sinΔλ) * t + (t = cosφ0 * sinφ1 - sinφ0 * cosφ1 * cosΔλ) * t), sinφ0 * sinφ1 + cosφ0 * cosφ1 * cosΔλ); }; d3.geo.graticule = function() { var x1, x0, X1, X0, y1, y0, Y1, Y0, dx = 10, dy = dx, DX = 90, DY = 360, x, y, X, Y, precision = 2.5; function graticule() { return { type: "MultiLineString", coordinates: lines() }; } function lines() { return d3.range(Math.ceil(X0 / DX) * DX, X1, DX).map(X).concat(d3.range(Math.ceil(Y0 / DY) * DY, Y1, DY).map(Y)).concat(d3.range(Math.ceil(x0 / dx) * dx, x1, dx).filter(function(x) { return abs(x % DX) > ε; }).map(x)).concat(d3.range(Math.ceil(y0 / dy) * dy, y1, dy).filter(function(y) { return abs(y % DY) > ε; }).map(y)); } graticule.lines = function() { return lines().map(function(coordinates) { return { type: "LineString", coordinates: coordinates }; }); }; graticule.outline = function() { return { type: "Polygon", coordinates: [ X(X0).concat(Y(Y1).slice(1), X(X1).reverse().slice(1), Y(Y0).reverse().slice(1)) ] }; }; graticule.extent = function(_) { if (!arguments.length) return graticule.minorExtent(); return graticule.majorExtent(_).minorExtent(_); }; graticule.majorExtent = function(_) { if (!arguments.length) return [ [ X0, Y0 ], [ X1, Y1 ] ]; X0 = +_[0][0], X1 = +_[1][0]; Y0 = +_[0][1], Y1 = +_[1][1]; if (X0 > X1) _ = X0, X0 = X1, X1 = _; if (Y0 > Y1) _ = Y0, Y0 = Y1, Y1 = _; return graticule.precision(precision); }; graticule.minorExtent = function(_) { if (!arguments.length) return [ [ x0, y0 ], [ x1, y1 ] ]; x0 = +_[0][0], x1 = +_[1][0]; y0 = +_[0][1], y1 = +_[1][1]; if (x0 > x1) _ = x0, x0 = x1, x1 = _; if (y0 > y1) _ = y0, y0 = y1, y1 = _; return graticule.precision(precision); }; graticule.step = function(_) { if (!arguments.length) return graticule.minorStep(); return graticule.majorStep(_).minorStep(_); }; graticule.majorStep = function(_) { if (!arguments.length) return [ DX, DY ]; DX = +_[0], DY = +_[1]; return graticule; }; graticule.minorStep = function(_) { if (!arguments.length) return [ dx, dy ]; dx = +_[0], dy = +_[1]; return graticule; }; graticule.precision = function(_) { if (!arguments.length) return precision; precision = +_; x = d3_geo_graticuleX(y0, y1, 90); y = d3_geo_graticuleY(x0, x1, precision); X = d3_geo_graticuleX(Y0, Y1, 90); Y = d3_geo_graticuleY(X0, X1, precision); return graticule; }; return graticule.majorExtent([ [ -180, -90 + ε ], [ 180, 90 - ε ] ]).minorExtent([ [ -180, -80 - ε ], [ 180, 80 + ε ] ]); }; function d3_geo_graticuleX(y0, y1, dy) { var y = d3.range(y0, y1 - ε, dy).concat(y1); return function(x) { return y.map(function(y) { return [ x, y ]; }); }; } function d3_geo_graticuleY(x0, x1, dx) { var x = d3.range(x0, x1 - ε, dx).concat(x1); return function(y) { return x.map(function(x) { return [ x, y ]; }); }; } function d3_source(d) { return d.source; } function d3_target(d) { return d.target; } d3.geo.greatArc = function() { var source = d3_source, source_, target = d3_target, target_; function greatArc() { return { type: "LineString", coordinates: [ source_ || source.apply(this, arguments), target_ || target.apply(this, arguments) ] }; } greatArc.distance = function() { return d3.geo.distance(source_ || source.apply(this, arguments), target_ || target.apply(this, arguments)); }; greatArc.source = function(_) { if (!arguments.length) return source; source = _, source_ = typeof _ === "function" ? null : _; return greatArc; }; greatArc.target = function(_) { if (!arguments.length) return target; target = _, target_ = typeof _ === "function" ? null : _; return greatArc; }; greatArc.precision = function() { return arguments.length ? greatArc : 0; }; return greatArc; }; d3.geo.interpolate = function(source, target) { return d3_geo_interpolate(source[0] * d3_radians, source[1] * d3_radians, target[0] * d3_radians, target[1] * d3_radians); }; function d3_geo_interpolate(x0, y0, x1, y1) { var cy0 = Math.cos(y0), sy0 = Math.sin(y0), cy1 = Math.cos(y1), sy1 = Math.sin(y1), kx0 = cy0 * Math.cos(x0), ky0 = cy0 * Math.sin(x0), kx1 = cy1 * Math.cos(x1), ky1 = cy1 * Math.sin(x1), d = 2 * Math.asin(Math.sqrt(d3_haversin(y1 - y0) + cy0 * cy1 * d3_haversin(x1 - x0))), k = 1 / Math.sin(d); var interpolate = d ? function(t) { var B = Math.sin(t *= d) * k, A = Math.sin(d - t) * k, x = A * kx0 + B * kx1, y = A * ky0 + B * ky1, z = A * sy0 + B * sy1; return [ Math.atan2(y, x) * d3_degrees, Math.atan2(z, Math.sqrt(x * x + y * y)) * d3_degrees ]; } : function() { return [ x0 * d3_degrees, y0 * d3_degrees ]; }; interpolate.distance = d; return interpolate; } d3.geo.length = function(object) { d3_geo_lengthSum = 0; d3.geo.stream(object, d3_geo_length); return d3_geo_lengthSum; }; var d3_geo_lengthSum; var d3_geo_length = { sphere: d3_noop, point: d3_noop, lineStart: d3_geo_lengthLineStart, lineEnd: d3_noop, polygonStart: d3_noop, polygonEnd: d3_noop }; function d3_geo_lengthLineStart() { var λ0, sinφ0, cosφ0; d3_geo_length.point = function(λ, φ) { λ0 = λ * d3_radians, sinφ0 = Math.sin(φ *= d3_radians), cosφ0 = Math.cos(φ); d3_geo_length.point = nextPoint; }; d3_geo_length.lineEnd = function() { d3_geo_length.point = d3_geo_length.lineEnd = d3_noop; }; function nextPoint(λ, φ) { var sinφ = Math.sin(φ *= d3_radians), cosφ = Math.cos(φ), t = abs((λ *= d3_radians) - λ0), cosΔλ = Math.cos(t); d3_geo_lengthSum += Math.atan2(Math.sqrt((t = cosφ * Math.sin(t)) * t + (t = cosφ0 * sinφ - sinφ0 * cosφ * cosΔλ) * t), sinφ0 * sinφ + cosφ0 * cosφ * cosΔλ); λ0 = λ, sinφ0 = sinφ, cosφ0 = cosφ; } } function d3_geo_azimuthal(scale, angle) { function azimuthal(λ, φ) { var cosλ = Math.cos(λ), cosφ = Math.cos(φ), k = scale(cosλ * cosφ); return [ k * cosφ * Math.sin(λ), k * Math.sin(φ) ]; } azimuthal.invert = function(x, y) { var ρ = Math.sqrt(x * x + y * y), c = angle(ρ), sinc = Math.sin(c), cosc = Math.cos(c); return [ Math.atan2(x * sinc, ρ * cosc), Math.asin(ρ && y * sinc / ρ) ]; }; return azimuthal; } var d3_geo_azimuthalEqualArea = d3_geo_azimuthal(function(cosλcosφ) { return Math.sqrt(2 / (1 + cosλcosφ)); }, function(ρ) { return 2 * Math.asin(ρ / 2); }); (d3.geo.azimuthalEqualArea = function() { return d3_geo_projection(d3_geo_azimuthalEqualArea); }).raw = d3_geo_azimuthalEqualArea; var d3_geo_azimuthalEquidistant = d3_geo_azimuthal(function(cosλcosφ) { var c = Math.acos(cosλcosφ); return c && c / Math.sin(c); }, d3_identity); (d3.geo.azimuthalEquidistant = function() { return d3_geo_projection(d3_geo_azimuthalEquidistant); }).raw = d3_geo_azimuthalEquidistant; function d3_geo_conicConformal(φ0, φ1) { var cosφ0 = Math.cos(φ0), t = function(φ) { return Math.tan(π / 4 + φ / 2); }, n = φ0 === φ1 ? Math.sin(φ0) : Math.log(cosφ0 / Math.cos(φ1)) / Math.log(t(φ1) / t(φ0)), F = cosφ0 * Math.pow(t(φ0), n) / n; if (!n) return d3_geo_mercator; function forward(λ, φ) { if (F > 0) { if (φ < -halfπ + ε) φ = -halfπ + ε; } else { if (φ > halfπ - ε) φ = halfπ - ε; } var ρ = F / Math.pow(t(φ), n); return [ ρ * Math.sin(n * λ), F - ρ * Math.cos(n * λ) ]; } forward.invert = function(x, y) { var ρ0_y = F - y, ρ = d3_sgn(n) * Math.sqrt(x * x + ρ0_y * ρ0_y); return [ Math.atan2(x, ρ0_y) / n, 2 * Math.atan(Math.pow(F / ρ, 1 / n)) - halfπ ]; }; return forward; } (d3.geo.conicConformal = function() { return d3_geo_conic(d3_geo_conicConformal); }).raw = d3_geo_conicConformal; function d3_geo_conicEquidistant(φ0, φ1) { var cosφ0 = Math.cos(φ0), n = φ0 === φ1 ? Math.sin(φ0) : (cosφ0 - Math.cos(φ1)) / (φ1 - φ0), G = cosφ0 / n + φ0; if (abs(n) < ε) return d3_geo_equirectangular; function forward(λ, φ) { var ρ = G - φ; return [ ρ * Math.sin(n * λ), G - ρ * Math.cos(n * λ) ]; } forward.invert = function(x, y) { var ρ0_y = G - y; return [ Math.atan2(x, ρ0_y) / n, G - d3_sgn(n) * Math.sqrt(x * x + ρ0_y * ρ0_y) ]; }; return forward; } (d3.geo.conicEquidistant = function() { return d3_geo_conic(d3_geo_conicEquidistant); }).raw = d3_geo_conicEquidistant; var d3_geo_gnomonic = d3_geo_azimuthal(function(cosλcosφ) { return 1 / cosλcosφ; }, Math.atan); (d3.geo.gnomonic = function() { return d3_geo_projection(d3_geo_gnomonic); }).raw = d3_geo_gnomonic; function d3_geo_mercator(λ, φ) { return [ λ, Math.log(Math.tan(π / 4 + φ / 2)) ]; } d3_geo_mercator.invert = function(x, y) { return [ x, 2 * Math.atan(Math.exp(y)) - halfπ ]; }; function d3_geo_mercatorProjection(project) { var m = d3_geo_projection(project), scale = m.scale, translate = m.translate, clipExtent = m.clipExtent, clipAuto; m.scale = function() { var v = scale.apply(m, arguments); return v === m ? clipAuto ? m.clipExtent(null) : m : v; }; m.translate = function() { var v = translate.apply(m, arguments); return v === m ? clipAuto ? m.clipExtent(null) : m : v; }; m.clipExtent = function(_) { var v = clipExtent.apply(m, arguments); if (v === m) { if (clipAuto = _ == null) { var k = π * scale(), t = translate(); clipExtent([ [ t[0] - k, t[1] - k ], [ t[0] + k, t[1] + k ] ]); } } else if (clipAuto) { v = null; } return v; }; return m.clipExtent(null); } (d3.geo.mercator = function() { return d3_geo_mercatorProjection(d3_geo_mercator); }).raw = d3_geo_mercator; var d3_geo_orthographic = d3_geo_azimuthal(function() { return 1; }, Math.asin); (d3.geo.orthographic = function() { return d3_geo_projection(d3_geo_orthographic); }).raw = d3_geo_orthographic; var d3_geo_stereographic = d3_geo_azimuthal(function(cosλcosφ) { return 1 / (1 + cosλcosφ); }, function(ρ) { return 2 * Math.atan(ρ); }); (d3.geo.stereographic = function() { return d3_geo_projection(d3_geo_stereographic); }).raw = d3_geo_stereographic; function d3_geo_transverseMercator(λ, φ) { return [ Math.log(Math.tan(π / 4 + φ / 2)), -λ ]; } d3_geo_transverseMercator.invert = function(x, y) { return [ -y, 2 * Math.atan(Math.exp(x)) - halfπ ]; }; (d3.geo.transverseMercator = function() { var projection = d3_geo_mercatorProjection(d3_geo_transverseMercator), center = projection.center, rotate = projection.rotate; projection.center = function(_) { return _ ? center([ -_[1], _[0] ]) : (_ = center(), [ _[1], -_[0] ]); }; projection.rotate = function(_) { return _ ? rotate([ _[0], _[1], _.length > 2 ? _[2] + 90 : 90 ]) : (_ = rotate(), [ _[0], _[1], _[2] - 90 ]); }; return rotate([ 0, 0, 90 ]); }).raw = d3_geo_transverseMercator; d3.geom = {}; function d3_geom_pointX(d) { return d[0]; } function d3_geom_pointY(d) { return d[1]; } d3.geom.hull = function(vertices) { var x = d3_geom_pointX, y = d3_geom_pointY; if (arguments.length) return hull(vertices); function hull(data) { if (data.length < 3) return []; var fx = d3_functor(x), fy = d3_functor(y), i, n = data.length, points = [], flippedPoints = []; for (i = 0; i < n; i++) { points.push([ +fx.call(this, data[i], i), +fy.call(this, data[i], i), i ]); } points.sort(d3_geom_hullOrder); for (i = 0; i < n; i++) flippedPoints.push([ points[i][0], -points[i][1] ]); var upper = d3_geom_hullUpper(points), lower = d3_geom_hullUpper(flippedPoints); var skipLeft = lower[0] === upper[0], skipRight = lower[lower.length - 1] === upper[upper.length - 1], polygon = []; for (i = upper.length - 1; i >= 0; --i) polygon.push(data[points[upper[i]][2]]); for (i = +skipLeft; i < lower.length - skipRight; ++i) polygon.push(data[points[lower[i]][2]]); return polygon; } hull.x = function(_) { return arguments.length ? (x = _, hull) : x; }; hull.y = function(_) { return arguments.length ? (y = _, hull) : y; }; return hull; }; function d3_geom_hullUpper(points) { var n = points.length, hull = [ 0, 1 ], hs = 2; for (var i = 2; i < n; i++) { while (hs > 1 && d3_cross2d(points[hull[hs - 2]], points[hull[hs - 1]], points[i]) <= 0) --hs; hull[hs++] = i; } return hull.slice(0, hs); } function d3_geom_hullOrder(a, b) { return a[0] - b[0] || a[1] - b[1]; } d3.geom.polygon = function(coordinates) { d3_subclass(coordinates, d3_geom_polygonPrototype); return coordinates; }; var d3_geom_polygonPrototype = d3.geom.polygon.prototype = []; d3_geom_polygonPrototype.area = function() { var i = -1, n = this.length, a, b = this[n - 1], area = 0; while (++i < n) { a = b; b = this[i]; area += a[1] * b[0] - a[0] * b[1]; } return area * .5; }; d3_geom_polygonPrototype.centroid = function(k) { var i = -1, n = this.length, x = 0, y = 0, a, b = this[n - 1], c; if (!arguments.length) k = -1 / (6 * this.area()); while (++i < n) { a = b; b = this[i]; c = a[0] * b[1] - b[0] * a[1]; x += (a[0] + b[0]) * c; y += (a[1] + b[1]) * c; } return [ x * k, y * k ]; }; d3_geom_polygonPrototype.clip = function(subject) { var input, closed = d3_geom_polygonClosed(subject), i = -1, n = this.length - d3_geom_polygonClosed(this), j, m, a = this[n - 1], b, c, d; while (++i < n) { input = subject.slice(); subject.length = 0; b = this[i]; c = input[(m = input.length - closed) - 1]; j = -1; while (++j < m) { d = input[j]; if (d3_geom_polygonInside(d, a, b)) { if (!d3_geom_polygonInside(c, a, b)) { subject.push(d3_geom_polygonIntersect(c, d, a, b)); } subject.push(d); } else if (d3_geom_polygonInside(c, a, b)) { subject.push(d3_geom_polygonIntersect(c, d, a, b)); } c = d; } if (closed) subject.push(subject[0]); a = b; } return subject; }; function d3_geom_polygonInside(p, a, b) { return (b[0] - a[0]) * (p[1] - a[1]) < (b[1] - a[1]) * (p[0] - a[0]); } function d3_geom_polygonIntersect(c, d, a, b) { var x1 = c[0], x3 = a[0], x21 = d[0] - x1, x43 = b[0] - x3, y1 = c[1], y3 = a[1], y21 = d[1] - y1, y43 = b[1] - y3, ua = (x43 * (y1 - y3) - y43 * (x1 - x3)) / (y43 * x21 - x43 * y21); return [ x1 + ua * x21, y1 + ua * y21 ]; } function d3_geom_polygonClosed(coordinates) { var a = coordinates[0], b = coordinates[coordinates.length - 1]; return !(a[0] - b[0] || a[1] - b[1]); } var d3_geom_voronoiEdges, d3_geom_voronoiCells, d3_geom_voronoiBeaches, d3_geom_voronoiBeachPool = [], d3_geom_voronoiFirstCircle, d3_geom_voronoiCircles, d3_geom_voronoiCirclePool = []; function d3_geom_voronoiBeach() { d3_geom_voronoiRedBlackNode(this); this.edge = this.site = this.circle = null; } function d3_geom_voronoiCreateBeach(site) { var beach = d3_geom_voronoiBeachPool.pop() || new d3_geom_voronoiBeach(); beach.site = site; return beach; } function d3_geom_voronoiDetachBeach(beach) { d3_geom_voronoiDetachCircle(beach); d3_geom_voronoiBeaches.remove(beach); d3_geom_voronoiBeachPool.push(beach); d3_geom_voronoiRedBlackNode(beach); } function d3_geom_voronoiRemoveBeach(beach) { var circle = beach.circle, x = circle.x, y = circle.cy, vertex = { x: x, y: y }, previous = beach.P, next = beach.N, disappearing = [ beach ]; d3_geom_voronoiDetachBeach(beach); var lArc = previous; while (lArc.circle && abs(x - lArc.circle.x) < ε && abs(y - lArc.circle.cy) < ε) { previous = lArc.P; disappearing.unshift(lArc); d3_geom_voronoiDetachBeach(lArc); lArc = previous; } disappearing.unshift(lArc); d3_geom_voronoiDetachCircle(lArc); var rArc = next; while (rArc.circle && abs(x - rArc.circle.x) < ε && abs(y - rArc.circle.cy) < ε) { next = rArc.N; disappearing.push(rArc); d3_geom_voronoiDetachBeach(rArc); rArc = next; } disappearing.push(rArc); d3_geom_voronoiDetachCircle(rArc); var nArcs = disappearing.length, iArc; for (iArc = 1; iArc < nArcs; ++iArc) { rArc = disappearing[iArc]; lArc = disappearing[iArc - 1]; d3_geom_voronoiSetEdgeEnd(rArc.edge, lArc.site, rArc.site, vertex); } lArc = disappearing[0]; rArc = disappearing[nArcs - 1]; rArc.edge = d3_geom_voronoiCreateEdge(lArc.site, rArc.site, null, vertex); d3_geom_voronoiAttachCircle(lArc); d3_geom_voronoiAttachCircle(rArc); } function d3_geom_voronoiAddBeach(site) { var x = site.x, directrix = site.y, lArc, rArc, dxl, dxr, node = d3_geom_voronoiBeaches._; while (node) { dxl = d3_geom_voronoiLeftBreakPoint(node, directrix) - x; if (dxl > ε) node = node.L; else { dxr = x - d3_geom_voronoiRightBreakPoint(node, directrix); if (dxr > ε) { if (!node.R) { lArc = node; break; } node = node.R; } else { if (dxl > -ε) { lArc = node.P; rArc = node; } else if (dxr > -ε) { lArc = node; rArc = node.N; } else { lArc = rArc = node; } break; } } } var newArc = d3_geom_voronoiCreateBeach(site); d3_geom_voronoiBeaches.insert(lArc, newArc); if (!lArc && !rArc) return; if (lArc === rArc) { d3_geom_voronoiDetachCircle(lArc); rArc = d3_geom_voronoiCreateBeach(lArc.site); d3_geom_voronoiBeaches.insert(newArc, rArc); newArc.edge = rArc.edge = d3_geom_voronoiCreateEdge(lArc.site, newArc.site); d3_geom_voronoiAttachCircle(lArc); d3_geom_voronoiAttachCircle(rArc); return; } if (!rArc) { newArc.edge = d3_geom_voronoiCreateEdge(lArc.site, newArc.site); return; } d3_geom_voronoiDetachCircle(lArc); d3_geom_voronoiDetachCircle(rArc); var lSite = lArc.site, ax = lSite.x, ay = lSite.y, bx = site.x - ax, by = site.y - ay, rSite = rArc.site, cx = rSite.x - ax, cy = rSite.y - ay, d = 2 * (bx * cy - by * cx), hb = bx * bx + by * by, hc = cx * cx + cy * cy, vertex = { x: (cy * hb - by * hc) / d + ax, y: (bx * hc - cx * hb) / d + ay }; d3_geom_voronoiSetEdgeEnd(rArc.edge, lSite, rSite, vertex); newArc.edge = d3_geom_voronoiCreateEdge(lSite, site, null, vertex); rArc.edge = d3_geom_voronoiCreateEdge(site, rSite, null, vertex); d3_geom_voronoiAttachCircle(lArc); d3_geom_voronoiAttachCircle(rArc); } function d3_geom_voronoiLeftBreakPoint(arc, directrix) { var site = arc.site, rfocx = site.x, rfocy = site.y, pby2 = rfocy - directrix; if (!pby2) return rfocx; var lArc = arc.P; if (!lArc) return -Infinity; site = lArc.site; var lfocx = site.x, lfocy = site.y, plby2 = lfocy - directrix; if (!plby2) return lfocx; var hl = lfocx - rfocx, aby2 = 1 / pby2 - 1 / plby2, b = hl / plby2; if (aby2) return (-b + Math.sqrt(b * b - 2 * aby2 * (hl * hl / (-2 * plby2) - lfocy + plby2 / 2 + rfocy - pby2 / 2))) / aby2 + rfocx; return (rfocx + lfocx) / 2; } function d3_geom_voronoiRightBreakPoint(arc, directrix) { var rArc = arc.N; if (rArc) return d3_geom_voronoiLeftBreakPoint(rArc, directrix); var site = arc.site; return site.y === directrix ? site.x : Infinity; } function d3_geom_voronoiCell(site) { this.site = site; this.edges = []; } d3_geom_voronoiCell.prototype.prepare = function() { var halfEdges = this.edges, iHalfEdge = halfEdges.length, edge; while (iHalfEdge--) { edge = halfEdges[iHalfEdge].edge; if (!edge.b || !edge.a) halfEdges.splice(iHalfEdge, 1); } halfEdges.sort(d3_geom_voronoiHalfEdgeOrder); return halfEdges.length; }; function d3_geom_voronoiCloseCells(extent) { var x0 = extent[0][0], x1 = extent[1][0], y0 = extent[0][1], y1 = extent[1][1], x2, y2, x3, y3, cells = d3_geom_voronoiCells, iCell = cells.length, cell, iHalfEdge, halfEdges, nHalfEdges, start, end; while (iCell--) { cell = cells[iCell]; if (!cell || !cell.prepare()) continue; halfEdges = cell.edges; nHalfEdges = halfEdges.length; iHalfEdge = 0; while (iHalfEdge < nHalfEdges) { end = halfEdges[iHalfEdge].end(), x3 = end.x, y3 = end.y; start = halfEdges[++iHalfEdge % nHalfEdges].start(), x2 = start.x, y2 = start.y; if (abs(x3 - x2) > ε || abs(y3 - y2) > ε) { halfEdges.splice(iHalfEdge, 0, new d3_geom_voronoiHalfEdge(d3_geom_voronoiCreateBorderEdge(cell.site, end, abs(x3 - x0) < ε && y1 - y3 > ε ? { x: x0, y: abs(x2 - x0) < ε ? y2 : y1 } : abs(y3 - y1) < ε && x1 - x3 > ε ? { x: abs(y2 - y1) < ε ? x2 : x1, y: y1 } : abs(x3 - x1) < ε && y3 - y0 > ε ? { x: x1, y: abs(x2 - x1) < ε ? y2 : y0 } : abs(y3 - y0) < ε && x3 - x0 > ε ? { x: abs(y2 - y0) < ε ? x2 : x0, y: y0 } : null), cell.site, null)); ++nHalfEdges; } } } } function d3_geom_voronoiHalfEdgeOrder(a, b) { return b.angle - a.angle; } function d3_geom_voronoiCircle() { d3_geom_voronoiRedBlackNode(this); this.x = this.y = this.arc = this.site = this.cy = null; } function d3_geom_voronoiAttachCircle(arc) { var lArc = arc.P, rArc = arc.N; if (!lArc || !rArc) return; var lSite = lArc.site, cSite = arc.site, rSite = rArc.site; if (lSite === rSite) return; var bx = cSite.x, by = cSite.y, ax = lSite.x - bx, ay = lSite.y - by, cx = rSite.x - bx, cy = rSite.y - by; var d = 2 * (ax * cy - ay * cx); if (d >= -ε2) return; var ha = ax * ax + ay * ay, hc = cx * cx + cy * cy, x = (cy * ha - ay * hc) / d, y = (ax * hc - cx * ha) / d, cy = y + by; var circle = d3_geom_voronoiCirclePool.pop() || new d3_geom_voronoiCircle(); circle.arc = arc; circle.site = cSite; circle.x = x + bx; circle.y = cy + Math.sqrt(x * x + y * y); circle.cy = cy; arc.circle = circle; var before = null, node = d3_geom_voronoiCircles._; while (node) { if (circle.y < node.y || circle.y === node.y && circle.x <= node.x) { if (node.L) node = node.L; else { before = node.P; break; } } else { if (node.R) node = node.R; else { before = node; break; } } } d3_geom_voronoiCircles.insert(before, circle); if (!before) d3_geom_voronoiFirstCircle = circle; } function d3_geom_voronoiDetachCircle(arc) { var circle = arc.circle; if (circle) { if (!circle.P) d3_geom_voronoiFirstCircle = circle.N; d3_geom_voronoiCircles.remove(circle); d3_geom_voronoiCirclePool.push(circle); d3_geom_voronoiRedBlackNode(circle); arc.circle = null; } } function d3_geom_voronoiClipEdges(extent) { var edges = d3_geom_voronoiEdges, clip = d3_geom_clipLine(extent[0][0], extent[0][1], extent[1][0], extent[1][1]), i = edges.length, e; while (i--) { e = edges[i]; if (!d3_geom_voronoiConnectEdge(e, extent) || !clip(e) || abs(e.a.x - e.b.x) < ε && abs(e.a.y - e.b.y) < ε) { e.a = e.b = null; edges.splice(i, 1); } } } function d3_geom_voronoiConnectEdge(edge, extent) { var vb = edge.b; if (vb) return true; var va = edge.a, x0 = extent[0][0], x1 = extent[1][0], y0 = extent[0][1], y1 = extent[1][1], lSite = edge.l, rSite = edge.r, lx = lSite.x, ly = lSite.y, rx = rSite.x, ry = rSite.y, fx = (lx + rx) / 2, fy = (ly + ry) / 2, fm, fb; if (ry === ly) { if (fx < x0 || fx >= x1) return; if (lx > rx) { if (!va) va = { x: fx, y: y0 }; else if (va.y >= y1) return; vb = { x: fx, y: y1 }; } else { if (!va) va = { x: fx, y: y1 }; else if (va.y < y0) return; vb = { x: fx, y: y0 }; } } else { fm = (lx - rx) / (ry - ly); fb = fy - fm * fx; if (fm < -1 || fm > 1) { if (lx > rx) { if (!va) va = { x: (y0 - fb) / fm, y: y0 }; else if (va.y >= y1) return; vb = { x: (y1 - fb) / fm, y: y1 }; } else { if (!va) va = { x: (y1 - fb) / fm, y: y1 }; else if (va.y < y0) return; vb = { x: (y0 - fb) / fm, y: y0 }; } } else { if (ly < ry) { if (!va) va = { x: x0, y: fm * x0 + fb }; else if (va.x >= x1) return; vb = { x: x1, y: fm * x1 + fb }; } else { if (!va) va = { x: x1, y: fm * x1 + fb }; else if (va.x < x0) return; vb = { x: x0, y: fm * x0 + fb }; } } } edge.a = va; edge.b = vb; return true; } function d3_geom_voronoiEdge(lSite, rSite) { this.l = lSite; this.r = rSite; this.a = this.b = null; } function d3_geom_voronoiCreateEdge(lSite, rSite, va, vb) { var edge = new d3_geom_voronoiEdge(lSite, rSite); d3_geom_voronoiEdges.push(edge); if (va) d3_geom_voronoiSetEdgeEnd(edge, lSite, rSite, va); if (vb) d3_geom_voronoiSetEdgeEnd(edge, rSite, lSite, vb); d3_geom_voronoiCells[lSite.i].edges.push(new d3_geom_voronoiHalfEdge(edge, lSite, rSite)); d3_geom_voronoiCells[rSite.i].edges.push(new d3_geom_voronoiHalfEdge(edge, rSite, lSite)); return edge; } function d3_geom_voronoiCreateBorderEdge(lSite, va, vb) { var edge = new d3_geom_voronoiEdge(lSite, null); edge.a = va; edge.b = vb; d3_geom_voronoiEdges.push(edge); return edge; } function d3_geom_voronoiSetEdgeEnd(edge, lSite, rSite, vertex) { if (!edge.a && !edge.b) { edge.a = vertex; edge.l = lSite; edge.r = rSite; } else if (edge.l === rSite) { edge.b = vertex; } else { edge.a = vertex; } } function d3_geom_voronoiHalfEdge(edge, lSite, rSite) { var va = edge.a, vb = edge.b; this.edge = edge; this.site = lSite; this.angle = rSite ? Math.atan2(rSite.y - lSite.y, rSite.x - lSite.x) : edge.l === lSite ? Math.atan2(vb.x - va.x, va.y - vb.y) : Math.atan2(va.x - vb.x, vb.y - va.y); } d3_geom_voronoiHalfEdge.prototype = { start: function() { return this.edge.l === this.site ? this.edge.a : this.edge.b; }, end: function() { return this.edge.l === this.site ? this.edge.b : this.edge.a; } }; function d3_geom_voronoiRedBlackTree() { this._ = null; } function d3_geom_voronoiRedBlackNode(node) { node.U = node.C = node.L = node.R = node.P = node.N = null; } d3_geom_voronoiRedBlackTree.prototype = { insert: function(after, node) { var parent, grandpa, uncle; if (after) { node.P = after; node.N = after.N; if (after.N) after.N.P = node; after.N = node; if (after.R) { after = after.R; while (after.L) after = after.L; after.L = node; } else { after.R = node; } parent = after; } else if (this._) { after = d3_geom_voronoiRedBlackFirst(this._); node.P = null; node.N = after; after.P = after.L = node; parent = after; } else { node.P = node.N = null; this._ = node; parent = null; } node.L = node.R = null; node.U = parent; node.C = true; after = node; while (parent && parent.C) { grandpa = parent.U; if (parent === grandpa.L) { uncle = grandpa.R; if (uncle && uncle.C) { parent.C = uncle.C = false; grandpa.C = true; after = grandpa; } else { if (after === parent.R) { d3_geom_voronoiRedBlackRotateLeft(this, parent); after = parent; parent = after.U; } parent.C = false; grandpa.C = true; d3_geom_voronoiRedBlackRotateRight(this, grandpa); } } else { uncle = grandpa.L; if (uncle && uncle.C) { parent.C = uncle.C = false; grandpa.C = true; after = grandpa; } else { if (after === parent.L) { d3_geom_voronoiRedBlackRotateRight(this, parent); after = parent; parent = after.U; } parent.C = false; grandpa.C = true; d3_geom_voronoiRedBlackRotateLeft(this, grandpa); } } parent = after.U; } this._.C = false; }, remove: function(node) { if (node.N) node.N.P = node.P; if (node.P) node.P.N = node.N; node.N = node.P = null; var parent = node.U, sibling, left = node.L, right = node.R, next, red; if (!left) next = right; else if (!right) next = left; else next = d3_geom_voronoiRedBlackFirst(right); if (parent) { if (parent.L === node) parent.L = next; else parent.R = next; } else { this._ = next; } if (left && right) { red = next.C; next.C = node.C; next.L = left; left.U = next; if (next !== right) { parent = next.U; next.U = node.U; node = next.R; parent.L = node; next.R = right; right.U = next; } else { next.U = parent; parent = next; node = next.R; } } else { red = node.C; node = next; } if (node) node.U = parent; if (red) return; if (node && node.C) { node.C = false; return; } do { if (node === this._) break; if (node === parent.L) { sibling = parent.R; if (sibling.C) { sibling.C = false; parent.C = true; d3_geom_voronoiRedBlackRotateLeft(this, parent); sibling = parent.R; } if (sibling.L && sibling.L.C || sibling.R && sibling.R.C) { if (!sibling.R || !sibling.R.C) { sibling.L.C = false; sibling.C = true; d3_geom_voronoiRedBlackRotateRight(this, sibling); sibling = parent.R; } sibling.C = parent.C; parent.C = sibling.R.C = false; d3_geom_voronoiRedBlackRotateLeft(this, parent); node = this._; break; } } else { sibling = parent.L; if (sibling.C) { sibling.C = false; parent.C = true; d3_geom_voronoiRedBlackRotateRight(this, parent); sibling = parent.L; } if (sibling.L && sibling.L.C || sibling.R && sibling.R.C) { if (!sibling.L || !sibling.L.C) { sibling.R.C = false; sibling.C = true; d3_geom_voronoiRedBlackRotateLeft(this, sibling); sibling = parent.L; } sibling.C = parent.C; parent.C = sibling.L.C = false; d3_geom_voronoiRedBlackRotateRight(this, parent); node = this._; break; } } sibling.C = true; node = parent; parent = parent.U; } while (!node.C); if (node) node.C = false; } }; function d3_geom_voronoiRedBlackRotateLeft(tree, node) { var p = node, q = node.R, parent = p.U; if (parent) { if (parent.L === p) parent.L = q; else parent.R = q; } else { tree._ = q; } q.U = parent; p.U = q; p.R = q.L; if (p.R) p.R.U = p; q.L = p; } function d3_geom_voronoiRedBlackRotateRight(tree, node) { var p = node, q = node.L, parent = p.U; if (parent) { if (parent.L === p) parent.L = q; else parent.R = q; } else { tree._ = q; } q.U = parent; p.U = q; p.L = q.R; if (p.L) p.L.U = p; q.R = p; } function d3_geom_voronoiRedBlackFirst(node) { while (node.L) node = node.L; return node; } function d3_geom_voronoi(sites, bbox) { var site = sites.sort(d3_geom_voronoiVertexOrder).pop(), x0, y0, circle; d3_geom_voronoiEdges = []; d3_geom_voronoiCells = new Array(sites.length); d3_geom_voronoiBeaches = new d3_geom_voronoiRedBlackTree(); d3_geom_voronoiCircles = new d3_geom_voronoiRedBlackTree(); while (true) { circle = d3_geom_voronoiFirstCircle; if (site && (!circle || site.y < circle.y || site.y === circle.y && site.x < circle.x)) { if (site.x !== x0 || site.y !== y0) { d3_geom_voronoiCells[site.i] = new d3_geom_voronoiCell(site); d3_geom_voronoiAddBeach(site); x0 = site.x, y0 = site.y; } site = sites.pop(); } else if (circle) { d3_geom_voronoiRemoveBeach(circle.arc); } else { break; } } if (bbox) d3_geom_voronoiClipEdges(bbox), d3_geom_voronoiCloseCells(bbox); var diagram = { cells: d3_geom_voronoiCells, edges: d3_geom_voronoiEdges }; d3_geom_voronoiBeaches = d3_geom_voronoiCircles = d3_geom_voronoiEdges = d3_geom_voronoiCells = null; return diagram; } function d3_geom_voronoiVertexOrder(a, b) { return b.y - a.y || b.x - a.x; } d3.geom.voronoi = function(points) { var x = d3_geom_pointX, y = d3_geom_pointY, fx = x, fy = y, clipExtent = d3_geom_voronoiClipExtent; if (points) return voronoi(points); function voronoi(data) { var polygons = new Array(data.length), x0 = clipExtent[0][0], y0 = clipExtent[0][1], x1 = clipExtent[1][0], y1 = clipExtent[1][1]; d3_geom_voronoi(sites(data), clipExtent).cells.forEach(function(cell, i) { var edges = cell.edges, site = cell.site, polygon = polygons[i] = edges.length ? edges.map(function(e) { var s = e.start(); return [ s.x, s.y ]; }) : site.x >= x0 && site.x <= x1 && site.y >= y0 && site.y <= y1 ? [ [ x0, y1 ], [ x1, y1 ], [ x1, y0 ], [ x0, y0 ] ] : []; polygon.point = data[i]; }); return polygons; } function sites(data) { return data.map(function(d, i) { return { x: Math.round(fx(d, i) / ε) * ε, y: Math.round(fy(d, i) / ε) * ε, i: i }; }); } voronoi.links = function(data) { return d3_geom_voronoi(sites(data)).edges.filter(function(edge) { return edge.l && edge.r; }).map(function(edge) { return { source: data[edge.l.i], target: data[edge.r.i] }; }); }; voronoi.triangles = function(data) { var triangles = []; d3_geom_voronoi(sites(data)).cells.forEach(function(cell, i) { var site = cell.site, edges = cell.edges.sort(d3_geom_voronoiHalfEdgeOrder), j = -1, m = edges.length, e0, s0, e1 = edges[m - 1].edge, s1 = e1.l === site ? e1.r : e1.l; while (++j < m) { e0 = e1; s0 = s1; e1 = edges[j].edge; s1 = e1.l === site ? e1.r : e1.l; if (i < s0.i && i < s1.i && d3_geom_voronoiTriangleArea(site, s0, s1) < 0) { triangles.push([ data[i], data[s0.i], data[s1.i] ]); } } }); return triangles; }; voronoi.x = function(_) { return arguments.length ? (fx = d3_functor(x = _), voronoi) : x; }; voronoi.y = function(_) { return arguments.length ? (fy = d3_functor(y = _), voronoi) : y; }; voronoi.clipExtent = function(_) { if (!arguments.length) return clipExtent === d3_geom_voronoiClipExtent ? null : clipExtent; clipExtent = _ == null ? d3_geom_voronoiClipExtent : _; return voronoi; }; voronoi.size = function(_) { if (!arguments.length) return clipExtent === d3_geom_voronoiClipExtent ? null : clipExtent && clipExtent[1]; return voronoi.clipExtent(_ && [ [ 0, 0 ], _ ]); }; return voronoi; }; var d3_geom_voronoiClipExtent = [ [ -1e6, -1e6 ], [ 1e6, 1e6 ] ]; function d3_geom_voronoiTriangleArea(a, b, c) { return (a.x - c.x) * (b.y - a.y) - (a.x - b.x) * (c.y - a.y); } d3.geom.delaunay = function(vertices) { return d3.geom.voronoi().triangles(vertices); }; d3.geom.quadtree = function(points, x1, y1, x2, y2) { var x = d3_geom_pointX, y = d3_geom_pointY, compat; if (compat = arguments.length) { x = d3_geom_quadtreeCompatX; y = d3_geom_quadtreeCompatY; if (compat === 3) { y2 = y1; x2 = x1; y1 = x1 = 0; } return quadtree(points); } function quadtree(data) { var d, fx = d3_functor(x), fy = d3_functor(y), xs, ys, i, n, x1_, y1_, x2_, y2_; if (x1 != null) { x1_ = x1, y1_ = y1, x2_ = x2, y2_ = y2; } else { x2_ = y2_ = -(x1_ = y1_ = Infinity); xs = [], ys = []; n = data.length; if (compat) for (i = 0; i < n; ++i) { d = data[i]; if (d.x < x1_) x1_ = d.x; if (d.y < y1_) y1_ = d.y; if (d.x > x2_) x2_ = d.x; if (d.y > y2_) y2_ = d.y; xs.push(d.x); ys.push(d.y); } else for (i = 0; i < n; ++i) { var x_ = +fx(d = data[i], i), y_ = +fy(d, i); if (x_ < x1_) x1_ = x_; if (y_ < y1_) y1_ = y_; if (x_ > x2_) x2_ = x_; if (y_ > y2_) y2_ = y_; xs.push(x_); ys.push(y_); } } var dx = x2_ - x1_, dy = y2_ - y1_; if (dx > dy) y2_ = y1_ + dx; else x2_ = x1_ + dy; function insert(n, d, x, y, x1, y1, x2, y2) { if (isNaN(x) || isNaN(y)) return; if (n.leaf) { var nx = n.x, ny = n.y; if (nx != null) { if (abs(nx - x) + abs(ny - y) < .01) { insertChild(n, d, x, y, x1, y1, x2, y2); } else { var nPoint = n.point; n.x = n.y = n.point = null; insertChild(n, nPoint, nx, ny, x1, y1, x2, y2); insertChild(n, d, x, y, x1, y1, x2, y2); } } else { n.x = x, n.y = y, n.point = d; } } else { insertChild(n, d, x, y, x1, y1, x2, y2); } } function insertChild(n, d, x, y, x1, y1, x2, y2) { var xm = (x1 + x2) * .5, ym = (y1 + y2) * .5, right = x >= xm, below = y >= ym, i = below << 1 | right; n.leaf = false; n = n.nodes[i] || (n.nodes[i] = d3_geom_quadtreeNode()); if (right) x1 = xm; else x2 = xm; if (below) y1 = ym; else y2 = ym; insert(n, d, x, y, x1, y1, x2, y2); } var root = d3_geom_quadtreeNode(); root.add = function(d) { insert(root, d, +fx(d, ++i), +fy(d, i), x1_, y1_, x2_, y2_); }; root.visit = function(f) { d3_geom_quadtreeVisit(f, root, x1_, y1_, x2_, y2_); }; root.find = function(point) { return d3_geom_quadtreeFind(root, point[0], point[1], x1_, y1_, x2_, y2_); }; i = -1; if (x1 == null) { while (++i < n) { insert(root, data[i], xs[i], ys[i], x1_, y1_, x2_, y2_); } --i; } else data.forEach(root.add); xs = ys = data = d = null; return root; } quadtree.x = function(_) { return arguments.length ? (x = _, quadtree) : x; }; quadtree.y = function(_) { return arguments.length ? (y = _, quadtree) : y; }; quadtree.extent = function(_) { if (!arguments.length) return x1 == null ? null : [ [ x1, y1 ], [ x2, y2 ] ]; if (_ == null) x1 = y1 = x2 = y2 = null; else x1 = +_[0][0], y1 = +_[0][1], x2 = +_[1][0], y2 = +_[1][1]; return quadtree; }; quadtree.size = function(_) { if (!arguments.length) return x1 == null ? null : [ x2 - x1, y2 - y1 ]; if (_ == null) x1 = y1 = x2 = y2 = null; else x1 = y1 = 0, x2 = +_[0], y2 = +_[1]; return quadtree; }; return quadtree; }; function d3_geom_quadtreeCompatX(d) { return d.x; } function d3_geom_quadtreeCompatY(d) { return d.y; } function d3_geom_quadtreeNode() { return { leaf: true, nodes: [], point: null, x: null, y: null }; } function d3_geom_quadtreeVisit(f, node, x1, y1, x2, y2) { if (!f(node, x1, y1, x2, y2)) { var sx = (x1 + x2) * .5, sy = (y1 + y2) * .5, children = node.nodes; if (children[0]) d3_geom_quadtreeVisit(f, children[0], x1, y1, sx, sy); if (children[1]) d3_geom_quadtreeVisit(f, children[1], sx, y1, x2, sy); if (children[2]) d3_geom_quadtreeVisit(f, children[2], x1, sy, sx, y2); if (children[3]) d3_geom_quadtreeVisit(f, children[3], sx, sy, x2, y2); } } function d3_geom_quadtreeFind(root, x, y, x0, y0, x3, y3) { var minDistance2 = Infinity, closestPoint; (function find(node, x1, y1, x2, y2) { if (x1 > x3 || y1 > y3 || x2 < x0 || y2 < y0) return; if (point = node.point) { var point, dx = x - node.x, dy = y - node.y, distance2 = dx * dx + dy * dy; if (distance2 < minDistance2) { var distance = Math.sqrt(minDistance2 = distance2); x0 = x - distance, y0 = y - distance; x3 = x + distance, y3 = y + distance; closestPoint = point; } } var children = node.nodes, xm = (x1 + x2) * .5, ym = (y1 + y2) * .5, right = x >= xm, below = y >= ym; for (var i = below << 1 | right, j = i + 4; i < j; ++i) { if (node = children[i & 3]) switch (i & 3) { case 0: find(node, x1, y1, xm, ym); break; case 1: find(node, xm, y1, x2, ym); break; case 2: find(node, x1, ym, xm, y2); break; case 3: find(node, xm, ym, x2, y2); break; } } })(root, x0, y0, x3, y3); return closestPoint; } d3.interpolateRgb = d3_interpolateRgb; function d3_interpolateRgb(a, b) { a = d3.rgb(a); b = d3.rgb(b); var ar = a.r, ag = a.g, ab = a.b, br = b.r - ar, bg = b.g - ag, bb = b.b - ab; return function(t) { return "#" + d3_rgb_hex(Math.round(ar + br * t)) + d3_rgb_hex(Math.round(ag + bg * t)) + d3_rgb_hex(Math.round(ab + bb * t)); }; } d3.interpolateObject = d3_interpolateObject; function d3_interpolateObject(a, b) { var i = {}, c = {}, k; for (k in a) { if (k in b) { i[k] = d3_interpolate(a[k], b[k]); } else { c[k] = a[k]; } } for (k in b) { if (!(k in a)) { c[k] = b[k]; } } return function(t) { for (k in i) c[k] = i[k](t); return c; }; } d3.interpolateNumber = d3_interpolateNumber; function d3_interpolateNumber(a, b) { a = +a, b = +b; return function(t) { return a * (1 - t) + b * t; }; } d3.interpolateString = d3_interpolateString; function d3_interpolateString(a, b) { var bi = d3_interpolate_numberA.lastIndex = d3_interpolate_numberB.lastIndex = 0, am, bm, bs, i = -1, s = [], q = []; a = a + "", b = b + ""; while ((am = d3_interpolate_numberA.exec(a)) && (bm = d3_interpolate_numberB.exec(b))) { if ((bs = bm.index) > bi) { bs = b.slice(bi, bs); if (s[i]) s[i] += bs; else s[++i] = bs; } if ((am = am[0]) === (bm = bm[0])) { if (s[i]) s[i] += bm; else s[++i] = bm; } else { s[++i] = null; q.push({ i: i, x: d3_interpolateNumber(am, bm) }); } bi = d3_interpolate_numberB.lastIndex; } if (bi < b.length) { bs = b.slice(bi); if (s[i]) s[i] += bs; else s[++i] = bs; } return s.length < 2 ? q[0] ? (b = q[0].x, function(t) { return b(t) + ""; }) : function() { return b; } : (b = q.length, function(t) { for (var i = 0, o; i < b; ++i) s[(o = q[i]).i] = o.x(t); return s.join(""); }); } var d3_interpolate_numberA = /[-+]?(?:\d+\.?\d*|\.?\d+)(?:[eE][-+]?\d+)?/g, d3_interpolate_numberB = new RegExp(d3_interpolate_numberA.source, "g"); d3.interpolate = d3_interpolate; function d3_interpolate(a, b) { var i = d3.interpolators.length, f; while (--i >= 0 && !(f = d3.interpolators[i](a, b))) ; return f; } d3.interpolators = [ function(a, b) { var t = typeof b; return (t === "string" ? d3_rgb_names.has(b.toLowerCase()) || /^(#|rgb\(|hsl\()/i.test(b) ? d3_interpolateRgb : d3_interpolateString : b instanceof d3_color ? d3_interpolateRgb : Array.isArray(b) ? d3_interpolateArray : t === "object" && isNaN(b) ? d3_interpolateObject : d3_interpolateNumber)(a, b); } ]; d3.interpolateArray = d3_interpolateArray; function d3_interpolateArray(a, b) { var x = [], c = [], na = a.length, nb = b.length, n0 = Math.min(a.length, b.length), i; for (i = 0; i < n0; ++i) x.push(d3_interpolate(a[i], b[i])); for (;i < na; ++i) c[i] = a[i]; for (;i < nb; ++i) c[i] = b[i]; return function(t) { for (i = 0; i < n0; ++i) c[i] = x[i](t); return c; }; } var d3_ease_default = function() { return d3_identity; }; var d3_ease = d3.map({ linear: d3_ease_default, poly: d3_ease_poly, quad: function() { return d3_ease_quad; }, cubic: function() { return d3_ease_cubic; }, sin: function() { return d3_ease_sin; }, exp: function() { return d3_ease_exp; }, circle: function() { return d3_ease_circle; }, elastic: d3_ease_elastic, back: d3_ease_back, bounce: function() { return d3_ease_bounce; } }); var d3_ease_mode = d3.map({ "in": d3_identity, out: d3_ease_reverse, "in-out": d3_ease_reflect, "out-in": function(f) { return d3_ease_reflect(d3_ease_reverse(f)); } }); d3.ease = function(name) { var i = name.indexOf("-"), t = i >= 0 ? name.slice(0, i) : name, m = i >= 0 ? name.slice(i + 1) : "in"; t = d3_ease.get(t) || d3_ease_default; m = d3_ease_mode.get(m) || d3_identity; return d3_ease_clamp(m(t.apply(null, d3_arraySlice.call(arguments, 1)))); }; function d3_ease_clamp(f) { return function(t) { return t <= 0 ? 0 : t >= 1 ? 1 : f(t); }; } function d3_ease_reverse(f) { return function(t) { return 1 - f(1 - t); }; } function d3_ease_reflect(f) { return function(t) { return .5 * (t < .5 ? f(2 * t) : 2 - f(2 - 2 * t)); }; } function d3_ease_quad(t) { return t * t; } function d3_ease_cubic(t) { return t * t * t; } function d3_ease_cubicInOut(t) { if (t <= 0) return 0; if (t >= 1) return 1; var t2 = t * t, t3 = t2 * t; return 4 * (t < .5 ? t3 : 3 * (t - t2) + t3 - .75); } function d3_ease_poly(e) { return function(t) { return Math.pow(t, e); }; } function d3_ease_sin(t) { return 1 - Math.cos(t * halfπ); } function d3_ease_exp(t) { return Math.pow(2, 10 * (t - 1)); } function d3_ease_circle(t) { return 1 - Math.sqrt(1 - t * t); } function d3_ease_elastic(a, p) { var s; if (arguments.length < 2) p = .45; if (arguments.length) s = p / τ * Math.asin(1 / a); else a = 1, s = p / 4; return function(t) { return 1 + a * Math.pow(2, -10 * t) * Math.sin((t - s) * τ / p); }; } function d3_ease_back(s) { if (!s) s = 1.70158; return function(t) { return t * t * ((s + 1) * t - s); }; } function d3_ease_bounce(t) { return t < 1 / 2.75 ? 7.5625 * t * t : t < 2 / 2.75 ? 7.5625 * (t -= 1.5 / 2.75) * t + .75 : t < 2.5 / 2.75 ? 7.5625 * (t -= 2.25 / 2.75) * t + .9375 : 7.5625 * (t -= 2.625 / 2.75) * t + .984375; } d3.interpolateHcl = d3_interpolateHcl; function d3_interpolateHcl(a, b) { a = d3.hcl(a); b = d3.hcl(b); var ah = a.h, ac = a.c, al = a.l, bh = b.h - ah, bc = b.c - ac, bl = b.l - al; if (isNaN(bc)) bc = 0, ac = isNaN(ac) ? b.c : ac; if (isNaN(bh)) bh = 0, ah = isNaN(ah) ? b.h : ah; else if (bh > 180) bh -= 360; else if (bh < -180) bh += 360; return function(t) { return d3_hcl_lab(ah + bh * t, ac + bc * t, al + bl * t) + ""; }; } d3.interpolateHsl = d3_interpolateHsl; function d3_interpolateHsl(a, b) { a = d3.hsl(a); b = d3.hsl(b); var ah = a.h, as = a.s, al = a.l, bh = b.h - ah, bs = b.s - as, bl = b.l - al; if (isNaN(bs)) bs = 0, as = isNaN(as) ? b.s : as; if (isNaN(bh)) bh = 0, ah = isNaN(ah) ? b.h : ah; else if (bh > 180) bh -= 360; else if (bh < -180) bh += 360; return function(t) { return d3_hsl_rgb(ah + bh * t, as + bs * t, al + bl * t) + ""; }; } d3.interpolateLab = d3_interpolateLab; function d3_interpolateLab(a, b) { a = d3.lab(a); b = d3.lab(b); var al = a.l, aa = a.a, ab = a.b, bl = b.l - al, ba = b.a - aa, bb = b.b - ab; return function(t) { return d3_lab_rgb(al + bl * t, aa + ba * t, ab + bb * t) + ""; }; } d3.interpolateRound = d3_interpolateRound; function d3_interpolateRound(a, b) { b -= a; return function(t) { return Math.round(a + b * t); }; } d3.transform = function(string) { var g = d3_document.createElementNS(d3.ns.prefix.svg, "g"); return (d3.transform = function(string) { if (string != null) { g.setAttribute("transform", string); var t = g.transform.baseVal.consolidate(); } return new d3_transform(t ? t.matrix : d3_transformIdentity); })(string); }; function d3_transform(m) { var r0 = [ m.a, m.b ], r1 = [ m.c, m.d ], kx = d3_transformNormalize(r0), kz = d3_transformDot(r0, r1), ky = d3_transformNormalize(d3_transformCombine(r1, r0, -kz)) || 0; if (r0[0] * r1[1] < r1[0] * r0[1]) { r0[0] *= -1; r0[1] *= -1; kx *= -1; kz *= -1; } this.rotate = (kx ? Math.atan2(r0[1], r0[0]) : Math.atan2(-r1[0], r1[1])) * d3_degrees; this.translate = [ m.e, m.f ]; this.scale = [ kx, ky ]; this.skew = ky ? Math.atan2(kz, ky) * d3_degrees : 0; } d3_transform.prototype.toString = function() { return "translate(" + this.translate + ")rotate(" + this.rotate + ")skewX(" + this.skew + ")scale(" + this.scale + ")"; }; function d3_transformDot(a, b) { return a[0] * b[0] + a[1] * b[1]; } function d3_transformNormalize(a) { var k = Math.sqrt(d3_transformDot(a, a)); if (k) { a[0] /= k; a[1] /= k; } return k; } function d3_transformCombine(a, b, k) { a[0] += k * b[0]; a[1] += k * b[1]; return a; } var d3_transformIdentity = { a: 1, b: 0, c: 0, d: 1, e: 0, f: 0 }; d3.interpolateTransform = d3_interpolateTransform; function d3_interpolateTransformPop(s) { return s.length ? s.pop() + "," : ""; } function d3_interpolateTranslate(ta, tb, s, q) { if (ta[0] !== tb[0] || ta[1] !== tb[1]) { var i = s.push("translate(", null, ",", null, ")"); q.push({ i: i - 4, x: d3_interpolateNumber(ta[0], tb[0]) }, { i: i - 2, x: d3_interpolateNumber(ta[1], tb[1]) }); } else if (tb[0] || tb[1]) { s.push("translate(" + tb + ")"); } } function d3_interpolateRotate(ra, rb, s, q) { if (ra !== rb) { if (ra - rb > 180) rb += 360; else if (rb - ra > 180) ra += 360; q.push({ i: s.push(d3_interpolateTransformPop(s) + "rotate(", null, ")") - 2, x: d3_interpolateNumber(ra, rb) }); } else if (rb) { s.push(d3_interpolateTransformPop(s) + "rotate(" + rb + ")"); } } function d3_interpolateSkew(wa, wb, s, q) { if (wa !== wb) { q.push({ i: s.push(d3_interpolateTransformPop(s) + "skewX(", null, ")") - 2, x: d3_interpolateNumber(wa, wb) }); } else if (wb) { s.push(d3_interpolateTransformPop(s) + "skewX(" + wb + ")"); } } function d3_interpolateScale(ka, kb, s, q) { if (ka[0] !== kb[0] || ka[1] !== kb[1]) { var i = s.push(d3_interpolateTransformPop(s) + "scale(", null, ",", null, ")"); q.push({ i: i - 4, x: d3_interpolateNumber(ka[0], kb[0]) }, { i: i - 2, x: d3_interpolateNumber(ka[1], kb[1]) }); } else if (kb[0] !== 1 || kb[1] !== 1) { s.push(d3_interpolateTransformPop(s) + "scale(" + kb + ")"); } } function d3_interpolateTransform(a, b) { var s = [], q = []; a = d3.transform(a), b = d3.transform(b); d3_interpolateTranslate(a.translate, b.translate, s, q); d3_interpolateRotate(a.rotate, b.rotate, s, q); d3_interpolateSkew(a.skew, b.skew, s, q); d3_interpolateScale(a.scale, b.scale, s, q); a = b = null; return function(t) { var i = -1, n = q.length, o; while (++i < n) s[(o = q[i]).i] = o.x(t); return s.join(""); }; } function d3_uninterpolateNumber(a, b) { b = (b -= a = +a) || 1 / b; return function(x) { return (x - a) / b; }; } function d3_uninterpolateClamp(a, b) { b = (b -= a = +a) || 1 / b; return function(x) { return Math.max(0, Math.min(1, (x - a) / b)); }; } d3.layout = {}; d3.layout.bundle = function() { return function(links) { var paths = [], i = -1, n = links.length; while (++i < n) paths.push(d3_layout_bundlePath(links[i])); return paths; }; }; function d3_layout_bundlePath(link) { var start = link.source, end = link.target, lca = d3_layout_bundleLeastCommonAncestor(start, end), points = [ start ]; while (start !== lca) { start = start.parent; points.push(start); } var k = points.length; while (end !== lca) { points.splice(k, 0, end); end = end.parent; } return points; } function d3_layout_bundleAncestors(node) { var ancestors = [], parent = node.parent; while (parent != null) { ancestors.push(node); node = parent; parent = parent.parent; } ancestors.push(node); return ancestors; } function d3_layout_bundleLeastCommonAncestor(a, b) { if (a === b) return a; var aNodes = d3_layout_bundleAncestors(a), bNodes = d3_layout_bundleAncestors(b), aNode = aNodes.pop(), bNode = bNodes.pop(), sharedNode = null; while (aNode === bNode) { sharedNode = aNode; aNode = aNodes.pop(); bNode = bNodes.pop(); } return sharedNode; } d3.layout.chord = function() { var chord = {}, chords, groups, matrix, n, padding = 0, sortGroups, sortSubgroups, sortChords; function relayout() { var subgroups = {}, groupSums = [], groupIndex = d3.range(n), subgroupIndex = [], k, x, x0, i, j; chords = []; groups = []; k = 0, i = -1; while (++i < n) { x = 0, j = -1; while (++j < n) { x += matrix[i][j]; } groupSums.push(x); subgroupIndex.push(d3.range(n)); k += x; } if (sortGroups) { groupIndex.sort(function(a, b) { return sortGroups(groupSums[a], groupSums[b]); }); } if (sortSubgroups) { subgroupIndex.forEach(function(d, i) { d.sort(function(a, b) { return sortSubgroups(matrix[i][a], matrix[i][b]); }); }); } k = (τ - padding * n) / k; x = 0, i = -1; while (++i < n) { x0 = x, j = -1; while (++j < n) { var di = groupIndex[i], dj = subgroupIndex[di][j], v = matrix[di][dj], a0 = x, a1 = x += v * k; subgroups[di + "-" + dj] = { index: di, subindex: dj, startAngle: a0, endAngle: a1, value: v }; } groups[di] = { index: di, startAngle: x0, endAngle: x, value: groupSums[di] }; x += padding; } i = -1; while (++i < n) { j = i - 1; while (++j < n) { var source = subgroups[i + "-" + j], target = subgroups[j + "-" + i]; if (source.value || target.value) { chords.push(source.value < target.value ? { source: target, target: source } : { source: source, target: target }); } } } if (sortChords) resort(); } function resort() { chords.sort(function(a, b) { return sortChords((a.source.value + a.target.value) / 2, (b.source.value + b.target.value) / 2); }); } chord.matrix = function(x) { if (!arguments.length) return matrix; n = (matrix = x) && matrix.length; chords = groups = null; return chord; }; chord.padding = function(x) { if (!arguments.length) return padding; padding = x; chords = groups = null; return chord; }; chord.sortGroups = function(x) { if (!arguments.length) return sortGroups; sortGroups = x; chords = groups = null; return chord; }; chord.sortSubgroups = function(x) { if (!arguments.length) return sortSubgroups; sortSubgroups = x; chords = null; return chord; }; chord.sortChords = function(x) { if (!arguments.length) return sortChords; sortChords = x; if (chords) resort(); return chord; }; chord.chords = function() { if (!chords) relayout(); return chords; }; chord.groups = function() { if (!groups) relayout(); return groups; }; return chord; }; d3.layout.force = function() { var force = {}, event = d3.dispatch("start", "tick", "end"), timer, size = [ 1, 1 ], drag, alpha, friction = .9, linkDistance = d3_layout_forceLinkDistance, linkStrength = d3_layout_forceLinkStrength, charge = -30, chargeDistance2 = d3_layout_forceChargeDistance2, gravity = .1, theta2 = .64, nodes = [], links = [], distances, strengths, charges; function repulse(node) { return function(quad, x1, _, x2) { if (quad.point !== node) { var dx = quad.cx - node.x, dy = quad.cy - node.y, dw = x2 - x1, dn = dx * dx + dy * dy; if (dw * dw / theta2 < dn) { if (dn < chargeDistance2) { var k = quad.charge / dn; node.px -= dx * k; node.py -= dy * k; } return true; } if (quad.point && dn && dn < chargeDistance2) { var k = quad.pointCharge / dn; node.px -= dx * k; node.py -= dy * k; } } return !quad.charge; }; } force.tick = function() { if ((alpha *= .99) < .005) { timer = null; event.end({ type: "end", alpha: alpha = 0 }); return true; } var n = nodes.length, m = links.length, q, i, o, s, t, l, k, x, y; for (i = 0; i < m; ++i) { o = links[i]; s = o.source; t = o.target; x = t.x - s.x; y = t.y - s.y; if (l = x * x + y * y) { l = alpha * strengths[i] * ((l = Math.sqrt(l)) - distances[i]) / l; x *= l; y *= l; t.x -= x * (k = s.weight + t.weight ? s.weight / (s.weight + t.weight) : .5); t.y -= y * k; s.x += x * (k = 1 - k); s.y += y * k; } } if (k = alpha * gravity) { x = size[0] / 2; y = size[1] / 2; i = -1; if (k) while (++i < n) { o = nodes[i]; o.x += (x - o.x) * k; o.y += (y - o.y) * k; } } if (charge) { d3_layout_forceAccumulate(q = d3.geom.quadtree(nodes), alpha, charges); i = -1; while (++i < n) { if (!(o = nodes[i]).fixed) { q.visit(repulse(o)); } } } i = -1; while (++i < n) { o = nodes[i]; if (o.fixed) { o.x = o.px; o.y = o.py; } else { o.x -= (o.px - (o.px = o.x)) * friction; o.y -= (o.py - (o.py = o.y)) * friction; } } event.tick({ type: "tick", alpha: alpha }); }; force.nodes = function(x) { if (!arguments.length) return nodes; nodes = x; return force; }; force.links = function(x) { if (!arguments.length) return links; links = x; return force; }; force.size = function(x) { if (!arguments.length) return size; size = x; return force; }; force.linkDistance = function(x) { if (!arguments.length) return linkDistance; linkDistance = typeof x === "function" ? x : +x; return force; }; force.distance = force.linkDistance; force.linkStrength = function(x) { if (!arguments.length) return linkStrength; linkStrength = typeof x === "function" ? x : +x; return force; }; force.friction = function(x) { if (!arguments.length) return friction; friction = +x; return force; }; force.charge = function(x) { if (!arguments.length) return charge; charge = typeof x === "function" ? x : +x; return force; }; force.chargeDistance = function(x) { if (!arguments.length) return Math.sqrt(chargeDistance2); chargeDistance2 = x * x; return force; }; force.gravity = function(x) { if (!arguments.length) return gravity; gravity = +x; return force; }; force.theta = function(x) { if (!arguments.length) return Math.sqrt(theta2); theta2 = x * x; return force; }; force.alpha = function(x) { if (!arguments.length) return alpha; x = +x; if (alpha) { if (x > 0) { alpha = x; } else { timer.c = null, timer.t = NaN, timer = null; event.end({ type: "end", alpha: alpha = 0 }); } } else if (x > 0) { event.start({ type: "start", alpha: alpha = x }); timer = d3_timer(force.tick); } return force; }; force.start = function() { var i, n = nodes.length, m = links.length, w = size[0], h = size[1], neighbors, o; for (i = 0; i < n; ++i) { (o = nodes[i]).index = i; o.weight = 0; } for (i = 0; i < m; ++i) { o = links[i]; if (typeof o.source == "number") o.source = nodes[o.source]; if (typeof o.target == "number") o.target = nodes[o.target]; ++o.source.weight; ++o.target.weight; } for (i = 0; i < n; ++i) { o = nodes[i]; if (isNaN(o.x)) o.x = position("x", w); if (isNaN(o.y)) o.y = position("y", h); if (isNaN(o.px)) o.px = o.x; if (isNaN(o.py)) o.py = o.y; } distances = []; if (typeof linkDistance === "function") for (i = 0; i < m; ++i) distances[i] = +linkDistance.call(this, links[i], i); else for (i = 0; i < m; ++i) distances[i] = linkDistance; strengths = []; if (typeof linkStrength === "function") for (i = 0; i < m; ++i) strengths[i] = +linkStrength.call(this, links[i], i); else for (i = 0; i < m; ++i) strengths[i] = linkStrength; charges = []; if (typeof charge === "function") for (i = 0; i < n; ++i) charges[i] = +charge.call(this, nodes[i], i); else for (i = 0; i < n; ++i) charges[i] = charge; function position(dimension, size) { if (!neighbors) { neighbors = new Array(n); for (j = 0; j < n; ++j) { neighbors[j] = []; } for (j = 0; j < m; ++j) { var o = links[j]; neighbors[o.source.index].push(o.target); neighbors[o.target.index].push(o.source); } } var candidates = neighbors[i], j = -1, l = candidates.length, x; while (++j < l) if (!isNaN(x = candidates[j][dimension])) return x; return Math.random() * size; } return force.resume(); }; force.resume = function() { return force.alpha(.1); }; force.stop = function() { return force.alpha(0); }; force.drag = function() { if (!drag) drag = d3.behavior.drag().origin(d3_identity).on("dragstart.force", d3_layout_forceDragstart).on("drag.force", dragmove).on("dragend.force", d3_layout_forceDragend); if (!arguments.length) return drag; this.on("mouseover.force", d3_layout_forceMouseover).on("mouseout.force", d3_layout_forceMouseout).call(drag); }; function dragmove(d) { d.px = d3.event.x, d.py = d3.event.y; force.resume(); } return d3.rebind(force, event, "on"); }; function d3_layout_forceDragstart(d) { d.fixed |= 2; } function d3_layout_forceDragend(d) { d.fixed &= ~6; } function d3_layout_forceMouseover(d) { d.fixed |= 4; d.px = d.x, d.py = d.y; } function d3_layout_forceMouseout(d) { d.fixed &= ~4; } function d3_layout_forceAccumulate(quad, alpha, charges) { var cx = 0, cy = 0; quad.charge = 0; if (!quad.leaf) { var nodes = quad.nodes, n = nodes.length, i = -1, c; while (++i < n) { c = nodes[i]; if (c == null) continue; d3_layout_forceAccumulate(c, alpha, charges); quad.charge += c.charge; cx += c.charge * c.cx; cy += c.charge * c.cy; } } if (quad.point) { if (!quad.leaf) { quad.point.x += Math.random() - .5; quad.point.y += Math.random() - .5; } var k = alpha * charges[quad.point.index]; quad.charge += quad.pointCharge = k; cx += k * quad.point.x; cy += k * quad.point.y; } quad.cx = cx / quad.charge; quad.cy = cy / quad.charge; } var d3_layout_forceLinkDistance = 20, d3_layout_forceLinkStrength = 1, d3_layout_forceChargeDistance2 = Infinity; d3.layout.hierarchy = function() { var sort = d3_layout_hierarchySort, children = d3_layout_hierarchyChildren, value = d3_layout_hierarchyValue; function hierarchy(root) { var stack = [ root ], nodes = [], node; root.depth = 0; while ((node = stack.pop()) != null) { nodes.push(node); if ((childs = children.call(hierarchy, node, node.depth)) && (n = childs.length)) { var n, childs, child; while (--n >= 0) { stack.push(child = childs[n]); child.parent = node; child.depth = node.depth + 1; } if (value) node.value = 0; node.children = childs; } else { if (value) node.value = +value.call(hierarchy, node, node.depth) || 0; delete node.children; } } d3_layout_hierarchyVisitAfter(root, function(node) { var childs, parent; if (sort && (childs = node.children)) childs.sort(sort); if (value && (parent = node.parent)) parent.value += node.value; }); return nodes; } hierarchy.sort = function(x) { if (!arguments.length) return sort; sort = x; return hierarchy; }; hierarchy.children = function(x) { if (!arguments.length) return children; children = x; return hierarchy; }; hierarchy.value = function(x) { if (!arguments.length) return value; value = x; return hierarchy; }; hierarchy.revalue = function(root) { if (value) { d3_layout_hierarchyVisitBefore(root, function(node) { if (node.children) node.value = 0; }); d3_layout_hierarchyVisitAfter(root, function(node) { var parent; if (!node.children) node.value = +value.call(hierarchy, node, node.depth) || 0; if (parent = node.parent) parent.value += node.value; }); } return root; }; return hierarchy; }; function d3_layout_hierarchyRebind(object, hierarchy) { d3.rebind(object, hierarchy, "sort", "children", "value"); object.nodes = object; object.links = d3_layout_hierarchyLinks; return object; } function d3_layout_hierarchyVisitBefore(node, callback) { var nodes = [ node ]; while ((node = nodes.pop()) != null) { callback(node); if ((children = node.children) && (n = children.length)) { var n, children; while (--n >= 0) nodes.push(children[n]); } } } function d3_layout_hierarchyVisitAfter(node, callback) { var nodes = [ node ], nodes2 = []; while ((node = nodes.pop()) != null) { nodes2.push(node); if ((children = node.children) && (n = children.length)) { var i = -1, n, children; while (++i < n) nodes.push(children[i]); } } while ((node = nodes2.pop()) != null) { callback(node); } } function d3_layout_hierarchyChildren(d) { return d.children; } function d3_layout_hierarchyValue(d) { return d.value; } function d3_layout_hierarchySort(a, b) { return b.value - a.value; } function d3_layout_hierarchyLinks(nodes) { return d3.merge(nodes.map(function(parent) { return (parent.children || []).map(function(child) { return { source: parent, target: child }; }); })); } d3.layout.partition = function() { var hierarchy = d3.layout.hierarchy(), size = [ 1, 1 ]; function position(node, x, dx, dy) { var children = node.children; node.x = x; node.y = node.depth * dy; node.dx = dx; node.dy = dy; if (children && (n = children.length)) { var i = -1, n, c, d; dx = node.value ? dx / node.value : 0; while (++i < n) { position(c = children[i], x, d = c.value * dx, dy); x += d; } } } function depth(node) { var children = node.children, d = 0; if (children && (n = children.length)) { var i = -1, n; while (++i < n) d = Math.max(d, depth(children[i])); } return 1 + d; } function partition(d, i) { var nodes = hierarchy.call(this, d, i); position(nodes[0], 0, size[0], size[1] / depth(nodes[0])); return nodes; } partition.size = function(x) { if (!arguments.length) return size; size = x; return partition; }; return d3_layout_hierarchyRebind(partition, hierarchy); }; d3.layout.pie = function() { var value = Number, sort = d3_layout_pieSortByValue, startAngle = 0, endAngle = τ, padAngle = 0; function pie(data) { var n = data.length, values = data.map(function(d, i) { return +value.call(pie, d, i); }), a = +(typeof startAngle === "function" ? startAngle.apply(this, arguments) : startAngle), da = (typeof endAngle === "function" ? endAngle.apply(this, arguments) : endAngle) - a, p = Math.min(Math.abs(da) / n, +(typeof padAngle === "function" ? padAngle.apply(this, arguments) : padAngle)), pa = p * (da < 0 ? -1 : 1), sum = d3.sum(values), k = sum ? (da - n * pa) / sum : 0, index = d3.range(n), arcs = [], v; if (sort != null) index.sort(sort === d3_layout_pieSortByValue ? function(i, j) { return values[j] - values[i]; } : function(i, j) { return sort(data[i], data[j]); }); index.forEach(function(i) { arcs[i] = { data: data[i], value: v = values[i], startAngle: a, endAngle: a += v * k + pa, padAngle: p }; }); return arcs; } pie.value = function(_) { if (!arguments.length) return value; value = _; return pie; }; pie.sort = function(_) { if (!arguments.length) return sort; sort = _; return pie; }; pie.startAngle = function(_) { if (!arguments.length) return startAngle; startAngle = _; return pie; }; pie.endAngle = function(_) { if (!arguments.length) return endAngle; endAngle = _; return pie; }; pie.padAngle = function(_) { if (!arguments.length) return padAngle; padAngle = _; return pie; }; return pie; }; var d3_layout_pieSortByValue = {}; d3.layout.stack = function() { var values = d3_identity, order = d3_layout_stackOrderDefault, offset = d3_layout_stackOffsetZero, out = d3_layout_stackOut, x = d3_layout_stackX, y = d3_layout_stackY; function stack(data, index) { if (!(n = data.length)) return data; var series = data.map(function(d, i) { return values.call(stack, d, i); }); var points = series.map(function(d) { return d.map(function(v, i) { return [ x.call(stack, v, i), y.call(stack, v, i) ]; }); }); var orders = order.call(stack, points, index); series = d3.permute(series, orders); points = d3.permute(points, orders); var offsets = offset.call(stack, points, index); var m = series[0].length, n, i, j, o; for (j = 0; j < m; ++j) { out.call(stack, series[0][j], o = offsets[j], points[0][j][1]); for (i = 1; i < n; ++i) { out.call(stack, series[i][j], o += points[i - 1][j][1], points[i][j][1]); } } return data; } stack.values = function(x) { if (!arguments.length) return values; values = x; return stack; }; stack.order = function(x) { if (!arguments.length) return order; order = typeof x === "function" ? x : d3_layout_stackOrders.get(x) || d3_layout_stackOrderDefault; return stack; }; stack.offset = function(x) { if (!arguments.length) return offset; offset = typeof x === "function" ? x : d3_layout_stackOffsets.get(x) || d3_layout_stackOffsetZero; return stack; }; stack.x = function(z) { if (!arguments.length) return x; x = z; return stack; }; stack.y = function(z) { if (!arguments.length) return y; y = z; return stack; }; stack.out = function(z) { if (!arguments.length) return out; out = z; return stack; }; return stack; }; function d3_layout_stackX(d) { return d.x; } function d3_layout_stackY(d) { return d.y; } function d3_layout_stackOut(d, y0, y) { d.y0 = y0; d.y = y; } var d3_layout_stackOrders = d3.map({ "inside-out": function(data) { var n = data.length, i, j, max = data.map(d3_layout_stackMaxIndex), sums = data.map(d3_layout_stackReduceSum), index = d3.range(n).sort(function(a, b) { return max[a] - max[b]; }), top = 0, bottom = 0, tops = [], bottoms = []; for (i = 0; i < n; ++i) { j = index[i]; if (top < bottom) { top += sums[j]; tops.push(j); } else { bottom += sums[j]; bottoms.push(j); } } return bottoms.reverse().concat(tops); }, reverse: function(data) { return d3.range(data.length).reverse(); }, "default": d3_layout_stackOrderDefault }); var d3_layout_stackOffsets = d3.map({ silhouette: function(data) { var n = data.length, m = data[0].length, sums = [], max = 0, i, j, o, y0 = []; for (j = 0; j < m; ++j) { for (i = 0, o = 0; i < n; i++) o += data[i][j][1]; if (o > max) max = o; sums.push(o); } for (j = 0; j < m; ++j) { y0[j] = (max - sums[j]) / 2; } return y0; }, wiggle: function(data) { var n = data.length, x = data[0], m = x.length, i, j, k, s1, s2, s3, dx, o, o0, y0 = []; y0[0] = o = o0 = 0; for (j = 1; j < m; ++j) { for (i = 0, s1 = 0; i < n; ++i) s1 += data[i][j][1]; for (i = 0, s2 = 0, dx = x[j][0] - x[j - 1][0]; i < n; ++i) { for (k = 0, s3 = (data[i][j][1] - data[i][j - 1][1]) / (2 * dx); k < i; ++k) { s3 += (data[k][j][1] - data[k][j - 1][1]) / dx; } s2 += s3 * data[i][j][1]; } y0[j] = o -= s1 ? s2 / s1 * dx : 0; if (o < o0) o0 = o; } for (j = 0; j < m; ++j) y0[j] -= o0; return y0; }, expand: function(data) { var n = data.length, m = data[0].length, k = 1 / n, i, j, o, y0 = []; for (j = 0; j < m; ++j) { for (i = 0, o = 0; i < n; i++) o += data[i][j][1]; if (o) for (i = 0; i < n; i++) data[i][j][1] /= o; else for (i = 0; i < n; i++) data[i][j][1] = k; } for (j = 0; j < m; ++j) y0[j] = 0; return y0; }, zero: d3_layout_stackOffsetZero }); function d3_layout_stackOrderDefault(data) { return d3.range(data.length); } function d3_layout_stackOffsetZero(data) { var j = -1, m = data[0].length, y0 = []; while (++j < m) y0[j] = 0; return y0; } function d3_layout_stackMaxIndex(array) { var i = 1, j = 0, v = array[0][1], k, n = array.length; for (;i < n; ++i) { if ((k = array[i][1]) > v) { j = i; v = k; } } return j; } function d3_layout_stackReduceSum(d) { return d.reduce(d3_layout_stackSum, 0); } function d3_layout_stackSum(p, d) { return p + d[1]; } d3.layout.histogram = function() { var frequency = true, valuer = Number, ranger = d3_layout_histogramRange, binner = d3_layout_histogramBinSturges; function histogram(data, i) { var bins = [], values = data.map(valuer, this), range = ranger.call(this, values, i), thresholds = binner.call(this, range, values, i), bin, i = -1, n = values.length, m = thresholds.length - 1, k = frequency ? 1 : 1 / n, x; while (++i < m) { bin = bins[i] = []; bin.dx = thresholds[i + 1] - (bin.x = thresholds[i]); bin.y = 0; } if (m > 0) { i = -1; while (++i < n) { x = values[i]; if (x >= range[0] && x <= range[1]) { bin = bins[d3.bisect(thresholds, x, 1, m) - 1]; bin.y += k; bin.push(data[i]); } } } return bins; } histogram.value = function(x) { if (!arguments.length) return valuer; valuer = x; return histogram; }; histogram.range = function(x) { if (!arguments.length) return ranger; ranger = d3_functor(x); return histogram; }; histogram.bins = function(x) { if (!arguments.length) return binner; binner = typeof x === "number" ? function(range) { return d3_layout_histogramBinFixed(range, x); } : d3_functor(x); return histogram; }; histogram.frequency = function(x) { if (!arguments.length) return frequency; frequency = !!x; return histogram; }; return histogram; }; function d3_layout_histogramBinSturges(range, values) { return d3_layout_histogramBinFixed(range, Math.ceil(Math.log(values.length) / Math.LN2 + 1)); } function d3_layout_histogramBinFixed(range, n) { var x = -1, b = +range[0], m = (range[1] - b) / n, f = []; while (++x <= n) f[x] = m * x + b; return f; } function d3_layout_histogramRange(values) { return [ d3.min(values), d3.max(values) ]; } d3.layout.pack = function() { var hierarchy = d3.layout.hierarchy().sort(d3_layout_packSort), padding = 0, size = [ 1, 1 ], radius; function pack(d, i) { var nodes = hierarchy.call(this, d, i), root = nodes[0], w = size[0], h = size[1], r = radius == null ? Math.sqrt : typeof radius === "function" ? radius : function() { return radius; }; root.x = root.y = 0; d3_layout_hierarchyVisitAfter(root, function(d) { d.r = +r(d.value); }); d3_layout_hierarchyVisitAfter(root, d3_layout_packSiblings); if (padding) { var dr = padding * (radius ? 1 : Math.max(2 * root.r / w, 2 * root.r / h)) / 2; d3_layout_hierarchyVisitAfter(root, function(d) { d.r += dr; }); d3_layout_hierarchyVisitAfter(root, d3_layout_packSiblings); d3_layout_hierarchyVisitAfter(root, function(d) { d.r -= dr; }); } d3_layout_packTransform(root, w / 2, h / 2, radius ? 1 : 1 / Math.max(2 * root.r / w, 2 * root.r / h)); return nodes; } pack.size = function(_) { if (!arguments.length) return size; size = _; return pack; }; pack.radius = function(_) { if (!arguments.length) return radius; radius = _ == null || typeof _ === "function" ? _ : +_; return pack; }; pack.padding = function(_) { if (!arguments.length) return padding; padding = +_; return pack; }; return d3_layout_hierarchyRebind(pack, hierarchy); }; function d3_layout_packSort(a, b) { return a.value - b.value; } function d3_layout_packInsert(a, b) { var c = a._pack_next; a._pack_next = b; b._pack_prev = a; b._pack_next = c; c._pack_prev = b; } function d3_layout_packSplice(a, b) { a._pack_next = b; b._pack_prev = a; } function d3_layout_packIntersects(a, b) { var dx = b.x - a.x, dy = b.y - a.y, dr = a.r + b.r; return .999 * dr * dr > dx * dx + dy * dy; } function d3_layout_packSiblings(node) { if (!(nodes = node.children) || !(n = nodes.length)) return; var nodes, xMin = Infinity, xMax = -Infinity, yMin = Infinity, yMax = -Infinity, a, b, c, i, j, k, n; function bound(node) { xMin = Math.min(node.x - node.r, xMin); xMax = Math.max(node.x + node.r, xMax); yMin = Math.min(node.y - node.r, yMin); yMax = Math.max(node.y + node.r, yMax); } nodes.forEach(d3_layout_packLink); a = nodes[0]; a.x = -a.r; a.y = 0; bound(a); if (n > 1) { b = nodes[1]; b.x = b.r; b.y = 0; bound(b); if (n > 2) { c = nodes[2]; d3_layout_packPlace(a, b, c); bound(c); d3_layout_packInsert(a, c); a._pack_prev = c; d3_layout_packInsert(c, b); b = a._pack_next; for (i = 3; i < n; i++) { d3_layout_packPlace(a, b, c = nodes[i]); var isect = 0, s1 = 1, s2 = 1; for (j = b._pack_next; j !== b; j = j._pack_next, s1++) { if (d3_layout_packIntersects(j, c)) { isect = 1; break; } } if (isect == 1) { for (k = a._pack_prev; k !== j._pack_prev; k = k._pack_prev, s2++) { if (d3_layout_packIntersects(k, c)) { break; } } } if (isect) { if (s1 < s2 || s1 == s2 && b.r < a.r) d3_layout_packSplice(a, b = j); else d3_layout_packSplice(a = k, b); i--; } else { d3_layout_packInsert(a, c); b = c; bound(c); } } } } var cx = (xMin + xMax) / 2, cy = (yMin + yMax) / 2, cr = 0; for (i = 0; i < n; i++) { c = nodes[i]; c.x -= cx; c.y -= cy; cr = Math.max(cr, c.r + Math.sqrt(c.x * c.x + c.y * c.y)); } node.r = cr; nodes.forEach(d3_layout_packUnlink); } function d3_layout_packLink(node) { node._pack_next = node._pack_prev = node; } function d3_layout_packUnlink(node) { delete node._pack_next; delete node._pack_prev; } function d3_layout_packTransform(node, x, y, k) { var children = node.children; node.x = x += k * node.x; node.y = y += k * node.y; node.r *= k; if (children) { var i = -1, n = children.length; while (++i < n) d3_layout_packTransform(children[i], x, y, k); } } function d3_layout_packPlace(a, b, c) { var db = a.r + c.r, dx = b.x - a.x, dy = b.y - a.y; if (db && (dx || dy)) { var da = b.r + c.r, dc = dx * dx + dy * dy; da *= da; db *= db; var x = .5 + (db - da) / (2 * dc), y = Math.sqrt(Math.max(0, 2 * da * (db + dc) - (db -= dc) * db - da * da)) / (2 * dc); c.x = a.x + x * dx + y * dy; c.y = a.y + x * dy - y * dx; } else { c.x = a.x + db; c.y = a.y; } } d3.layout.tree = function() { var hierarchy = d3.layout.hierarchy().sort(null).value(null), separation = d3_layout_treeSeparation, size = [ 1, 1 ], nodeSize = null; function tree(d, i) { var nodes = hierarchy.call(this, d, i), root0 = nodes[0], root1 = wrapTree(root0); d3_layout_hierarchyVisitAfter(root1, firstWalk), root1.parent.m = -root1.z; d3_layout_hierarchyVisitBefore(root1, secondWalk); if (nodeSize) d3_layout_hierarchyVisitBefore(root0, sizeNode); else { var left = root0, right = root0, bottom = root0; d3_layout_hierarchyVisitBefore(root0, function(node) { if (node.x < left.x) left = node; if (node.x > right.x) right = node; if (node.depth > bottom.depth) bottom = node; }); var tx = separation(left, right) / 2 - left.x, kx = size[0] / (right.x + separation(right, left) / 2 + tx), ky = size[1] / (bottom.depth || 1); d3_layout_hierarchyVisitBefore(root0, function(node) { node.x = (node.x + tx) * kx; node.y = node.depth * ky; }); } return nodes; } function wrapTree(root0) { var root1 = { A: null, children: [ root0 ] }, queue = [ root1 ], node1; while ((node1 = queue.pop()) != null) { for (var children = node1.children, child, i = 0, n = children.length; i < n; ++i) { queue.push((children[i] = child = { _: children[i], parent: node1, children: (child = children[i].children) && child.slice() || [], A: null, a: null, z: 0, m: 0, c: 0, s: 0, t: null, i: i }).a = child); } } return root1.children[0]; } function firstWalk(v) { var children = v.children, siblings = v.parent.children, w = v.i ? siblings[v.i - 1] : null; if (children.length) { d3_layout_treeShift(v); var midpoint = (children[0].z + children[children.length - 1].z) / 2; if (w) { v.z = w.z + separation(v._, w._); v.m = v.z - midpoint; } else { v.z = midpoint; } } else if (w) { v.z = w.z + separation(v._, w._); } v.parent.A = apportion(v, w, v.parent.A || siblings[0]); } function secondWalk(v) { v._.x = v.z + v.parent.m; v.m += v.parent.m; } function apportion(v, w, ancestor) { if (w) { var vip = v, vop = v, vim = w, vom = vip.parent.children[0], sip = vip.m, sop = vop.m, sim = vim.m, som = vom.m, shift; while (vim = d3_layout_treeRight(vim), vip = d3_layout_treeLeft(vip), vim && vip) { vom = d3_layout_treeLeft(vom); vop = d3_layout_treeRight(vop); vop.a = v; shift = vim.z + sim - vip.z - sip + separation(vim._, vip._); if (shift > 0) { d3_layout_treeMove(d3_layout_treeAncestor(vim, v, ancestor), v, shift); sip += shift; sop += shift; } sim += vim.m; sip += vip.m; som += vom.m; sop += vop.m; } if (vim && !d3_layout_treeRight(vop)) { vop.t = vim; vop.m += sim - sop; } if (vip && !d3_layout_treeLeft(vom)) { vom.t = vip; vom.m += sip - som; ancestor = v; } } return ancestor; } function sizeNode(node) { node.x *= size[0]; node.y = node.depth * size[1]; } tree.separation = function(x) { if (!arguments.length) return separation; separation = x; return tree; }; tree.size = function(x) { if (!arguments.length) return nodeSize ? null : size; nodeSize = (size = x) == null ? sizeNode : null; return tree; }; tree.nodeSize = function(x) { if (!arguments.length) return nodeSize ? size : null; nodeSize = (size = x) == null ? null : sizeNode; return tree; }; return d3_layout_hierarchyRebind(tree, hierarchy); }; function d3_layout_treeSeparation(a, b) { return a.parent == b.parent ? 1 : 2; } function d3_layout_treeLeft(v) { var children = v.children; return children.length ? children[0] : v.t; } function d3_layout_treeRight(v) { var children = v.children, n; return (n = children.length) ? children[n - 1] : v.t; } function d3_layout_treeMove(wm, wp, shift) { var change = shift / (wp.i - wm.i); wp.c -= change; wp.s += shift; wm.c += change; wp.z += shift; wp.m += shift; } function d3_layout_treeShift(v) { var shift = 0, change = 0, children = v.children, i = children.length, w; while (--i >= 0) { w = children[i]; w.z += shift; w.m += shift; shift += w.s + (change += w.c); } } function d3_layout_treeAncestor(vim, v, ancestor) { return vim.a.parent === v.parent ? vim.a : ancestor; } d3.layout.cluster = function() { var hierarchy = d3.layout.hierarchy().sort(null).value(null), separation = d3_layout_treeSeparation, size = [ 1, 1 ], nodeSize = false; function cluster(d, i) { var nodes = hierarchy.call(this, d, i), root = nodes[0], previousNode, x = 0; d3_layout_hierarchyVisitAfter(root, function(node) { var children = node.children; if (children && children.length) { node.x = d3_layout_clusterX(children); node.y = d3_layout_clusterY(children); } else { node.x = previousNode ? x += separation(node, previousNode) : 0; node.y = 0; previousNode = node; } }); var left = d3_layout_clusterLeft(root), right = d3_layout_clusterRight(root), x0 = left.x - separation(left, right) / 2, x1 = right.x + separation(right, left) / 2; d3_layout_hierarchyVisitAfter(root, nodeSize ? function(node) { node.x = (node.x - root.x) * size[0]; node.y = (root.y - node.y) * size[1]; } : function(node) { node.x = (node.x - x0) / (x1 - x0) * size[0]; node.y = (1 - (root.y ? node.y / root.y : 1)) * size[1]; }); return nodes; } cluster.separation = function(x) { if (!arguments.length) return separation; separation = x; return cluster; }; cluster.size = function(x) { if (!arguments.length) return nodeSize ? null : size; nodeSize = (size = x) == null; return cluster; }; cluster.nodeSize = function(x) { if (!arguments.length) return nodeSize ? size : null; nodeSize = (size = x) != null; return cluster; }; return d3_layout_hierarchyRebind(cluster, hierarchy); }; function d3_layout_clusterY(children) { return 1 + d3.max(children, function(child) { return child.y; }); } function d3_layout_clusterX(children) { return children.reduce(function(x, child) { return x + child.x; }, 0) / children.length; } function d3_layout_clusterLeft(node) { var children = node.children; return children && children.length ? d3_layout_clusterLeft(children[0]) : node; } function d3_layout_clusterRight(node) { var children = node.children, n; return children && (n = children.length) ? d3_layout_clusterRight(children[n - 1]) : node; } d3.layout.treemap = function() { var hierarchy = d3.layout.hierarchy(), round = Math.round, size = [ 1, 1 ], padding = null, pad = d3_layout_treemapPadNull, sticky = false, stickies, mode = "squarify", ratio = .5 * (1 + Math.sqrt(5)); function scale(children, k) { var i = -1, n = children.length, child, area; while (++i < n) { area = (child = children[i]).value * (k < 0 ? 0 : k); child.area = isNaN(area) || area <= 0 ? 0 : area; } } function squarify(node) { var children = node.children; if (children && children.length) { var rect = pad(node), row = [], remaining = children.slice(), child, best = Infinity, score, u = mode === "slice" ? rect.dx : mode === "dice" ? rect.dy : mode === "slice-dice" ? node.depth & 1 ? rect.dy : rect.dx : Math.min(rect.dx, rect.dy), n; scale(remaining, rect.dx * rect.dy / node.value); row.area = 0; while ((n = remaining.length) > 0) { row.push(child = remaining[n - 1]); row.area += child.area; if (mode !== "squarify" || (score = worst(row, u)) <= best) { remaining.pop(); best = score; } else { row.area -= row.pop().area; position(row, u, rect, false); u = Math.min(rect.dx, rect.dy); row.length = row.area = 0; best = Infinity; } } if (row.length) { position(row, u, rect, true); row.length = row.area = 0; } children.forEach(squarify); } } function stickify(node) { var children = node.children; if (children && children.length) { var rect = pad(node), remaining = children.slice(), child, row = []; scale(remaining, rect.dx * rect.dy / node.value); row.area = 0; while (child = remaining.pop()) { row.push(child); row.area += child.area; if (child.z != null) { position(row, child.z ? rect.dx : rect.dy, rect, !remaining.length); row.length = row.area = 0; } } children.forEach(stickify); } } function worst(row, u) { var s = row.area, r, rmax = 0, rmin = Infinity, i = -1, n = row.length; while (++i < n) { if (!(r = row[i].area)) continue; if (r < rmin) rmin = r; if (r > rmax) rmax = r; } s *= s; u *= u; return s ? Math.max(u * rmax * ratio / s, s / (u * rmin * ratio)) : Infinity; } function position(row, u, rect, flush) { var i = -1, n = row.length, x = rect.x, y = rect.y, v = u ? round(row.area / u) : 0, o; if (u == rect.dx) { if (flush || v > rect.dy) v = rect.dy; while (++i < n) { o = row[i]; o.x = x; o.y = y; o.dy = v; x += o.dx = Math.min(rect.x + rect.dx - x, v ? round(o.area / v) : 0); } o.z = true; o.dx += rect.x + rect.dx - x; rect.y += v; rect.dy -= v; } else { if (flush || v > rect.dx) v = rect.dx; while (++i < n) { o = row[i]; o.x = x; o.y = y; o.dx = v; y += o.dy = Math.min(rect.y + rect.dy - y, v ? round(o.area / v) : 0); } o.z = false; o.dy += rect.y + rect.dy - y; rect.x += v; rect.dx -= v; } } function treemap(d) { var nodes = stickies || hierarchy(d), root = nodes[0]; root.x = root.y = 0; if (root.value) root.dx = size[0], root.dy = size[1]; else root.dx = root.dy = 0; if (stickies) hierarchy.revalue(root); scale([ root ], root.dx * root.dy / root.value); (stickies ? stickify : squarify)(root); if (sticky) stickies = nodes; return nodes; } treemap.size = function(x) { if (!arguments.length) return size; size = x; return treemap; }; treemap.padding = function(x) { if (!arguments.length) return padding; function padFunction(node) { var p = x.call(treemap, node, node.depth); return p == null ? d3_layout_treemapPadNull(node) : d3_layout_treemapPad(node, typeof p === "number" ? [ p, p, p, p ] : p); } function padConstant(node) { return d3_layout_treemapPad(node, x); } var type; pad = (padding = x) == null ? d3_layout_treemapPadNull : (type = typeof x) === "function" ? padFunction : type === "number" ? (x = [ x, x, x, x ], padConstant) : padConstant; return treemap; }; treemap.round = function(x) { if (!arguments.length) return round != Number; round = x ? Math.round : Number; return treemap; }; treemap.sticky = function(x) { if (!arguments.length) return sticky; sticky = x; stickies = null; return treemap; }; treemap.ratio = function(x) { if (!arguments.length) return ratio; ratio = x; return treemap; }; treemap.mode = function(x) { if (!arguments.length) return mode; mode = x + ""; return treemap; }; return d3_layout_hierarchyRebind(treemap, hierarchy); }; function d3_layout_treemapPadNull(node) { return { x: node.x, y: node.y, dx: node.dx, dy: node.dy }; } function d3_layout_treemapPad(node, padding) { var x = node.x + padding[3], y = node.y + padding[0], dx = node.dx - padding[1] - padding[3], dy = node.dy - padding[0] - padding[2]; if (dx < 0) { x += dx / 2; dx = 0; } if (dy < 0) { y += dy / 2; dy = 0; } return { x: x, y: y, dx: dx, dy: dy }; } d3.random = { normal: function(µ, σ) { var n = arguments.length; if (n < 2) σ = 1; if (n < 1) µ = 0; return function() { var x, y, r; do { x = Math.random() * 2 - 1; y = Math.random() * 2 - 1; r = x * x + y * y; } while (!r || r > 1); return µ + σ * x * Math.sqrt(-2 * Math.log(r) / r); }; }, logNormal: function() { var random = d3.random.normal.apply(d3, arguments); return function() { return Math.exp(random()); }; }, bates: function(m) { var random = d3.random.irwinHall(m); return function() { return random() / m; }; }, irwinHall: function(m) { return function() { for (var s = 0, j = 0; j < m; j++) s += Math.random(); return s; }; } }; d3.scale = {}; function d3_scaleExtent(domain) { var start = domain[0], stop = domain[domain.length - 1]; return start < stop ? [ start, stop ] : [ stop, start ]; } function d3_scaleRange(scale) { return scale.rangeExtent ? scale.rangeExtent() : d3_scaleExtent(scale.range()); } function d3_scale_bilinear(domain, range, uninterpolate, interpolate) { var u = uninterpolate(domain[0], domain[1]), i = interpolate(range[0], range[1]); return function(x) { return i(u(x)); }; } function d3_scale_nice(domain, nice) { var i0 = 0, i1 = domain.length - 1, x0 = domain[i0], x1 = domain[i1], dx; if (x1 < x0) { dx = i0, i0 = i1, i1 = dx; dx = x0, x0 = x1, x1 = dx; } domain[i0] = nice.floor(x0); domain[i1] = nice.ceil(x1); return domain; } function d3_scale_niceStep(step) { return step ? { floor: function(x) { return Math.floor(x / step) * step; }, ceil: function(x) { return Math.ceil(x / step) * step; } } : d3_scale_niceIdentity; } var d3_scale_niceIdentity = { floor: d3_identity, ceil: d3_identity }; function d3_scale_polylinear(domain, range, uninterpolate, interpolate) { var u = [], i = [], j = 0, k = Math.min(domain.length, range.length) - 1; if (domain[k] < domain[0]) { domain = domain.slice().reverse(); range = range.slice().reverse(); } while (++j <= k) { u.push(uninterpolate(domain[j - 1], domain[j])); i.push(interpolate(range[j - 1], range[j])); } return function(x) { var j = d3.bisect(domain, x, 1, k) - 1; return i[j](u[j](x)); }; } d3.scale.linear = function() { return d3_scale_linear([ 0, 1 ], [ 0, 1 ], d3_interpolate, false); }; function d3_scale_linear(domain, range, interpolate, clamp) { var output, input; function rescale() { var linear = Math.min(domain.length, range.length) > 2 ? d3_scale_polylinear : d3_scale_bilinear, uninterpolate = clamp ? d3_uninterpolateClamp : d3_uninterpolateNumber; output = linear(domain, range, uninterpolate, interpolate); input = linear(range, domain, uninterpolate, d3_interpolate); return scale; } function scale(x) { return output(x); } scale.invert = function(y) { return input(y); }; scale.domain = function(x) { if (!arguments.length) return domain; domain = x.map(Number); return rescale(); }; scale.range = function(x) { if (!arguments.length) return range; range = x; return rescale(); }; scale.rangeRound = function(x) { return scale.range(x).interpolate(d3_interpolateRound); }; scale.clamp = function(x) { if (!arguments.length) return clamp; clamp = x; return rescale(); }; scale.interpolate = function(x) { if (!arguments.length) return interpolate; interpolate = x; return rescale(); }; scale.ticks = function(m) { return d3_scale_linearTicks(domain, m); }; scale.tickFormat = function(m, format) { return d3_scale_linearTickFormat(domain, m, format); }; scale.nice = function(m) { d3_scale_linearNice(domain, m); return rescale(); }; scale.copy = function() { return d3_scale_linear(domain, range, interpolate, clamp); }; return rescale(); } function d3_scale_linearRebind(scale, linear) { return d3.rebind(scale, linear, "range", "rangeRound", "interpolate", "clamp"); } function d3_scale_linearNice(domain, m) { d3_scale_nice(domain, d3_scale_niceStep(d3_scale_linearTickRange(domain, m)[2])); d3_scale_nice(domain, d3_scale_niceStep(d3_scale_linearTickRange(domain, m)[2])); return domain; } function d3_scale_linearTickRange(domain, m) { if (m == null) m = 10; var extent = d3_scaleExtent(domain), span = extent[1] - extent[0], step = Math.pow(10, Math.floor(Math.log(span / m) / Math.LN10)), err = m / span * step; if (err <= .15) step *= 10; else if (err <= .35) step *= 5; else if (err <= .75) step *= 2; extent[0] = Math.ceil(extent[0] / step) * step; extent[1] = Math.floor(extent[1] / step) * step + step * .5; extent[2] = step; return extent; } function d3_scale_linearTicks(domain, m) { return d3.range.apply(d3, d3_scale_linearTickRange(domain, m)); } function d3_scale_linearTickFormat(domain, m, format) { var range = d3_scale_linearTickRange(domain, m); if (format) { var match = d3_format_re.exec(format); match.shift(); if (match[8] === "s") { var prefix = d3.formatPrefix(Math.max(abs(range[0]), abs(range[1]))); if (!match[7]) match[7] = "." + d3_scale_linearPrecision(prefix.scale(range[2])); match[8] = "f"; format = d3.format(match.join("")); return function(d) { return format(prefix.scale(d)) + prefix.symbol; }; } if (!match[7]) match[7] = "." + d3_scale_linearFormatPrecision(match[8], range); format = match.join(""); } else { format = ",." + d3_scale_linearPrecision(range[2]) + "f"; } return d3.format(format); } var d3_scale_linearFormatSignificant = { s: 1, g: 1, p: 1, r: 1, e: 1 }; function d3_scale_linearPrecision(value) { return -Math.floor(Math.log(value) / Math.LN10 + .01); } function d3_scale_linearFormatPrecision(type, range) { var p = d3_scale_linearPrecision(range[2]); return type in d3_scale_linearFormatSignificant ? Math.abs(p - d3_scale_linearPrecision(Math.max(abs(range[0]), abs(range[1])))) + +(type !== "e") : p - (type === "%") * 2; } d3.scale.log = function() { return d3_scale_log(d3.scale.linear().domain([ 0, 1 ]), 10, true, [ 1, 10 ]); }; function d3_scale_log(linear, base, positive, domain) { function log(x) { return (positive ? Math.log(x < 0 ? 0 : x) : -Math.log(x > 0 ? 0 : -x)) / Math.log(base); } function pow(x) { return positive ? Math.pow(base, x) : -Math.pow(base, -x); } function scale(x) { return linear(log(x)); } scale.invert = function(x) { return pow(linear.invert(x)); }; scale.domain = function(x) { if (!arguments.length) return domain; positive = x[0] >= 0; linear.domain((domain = x.map(Number)).map(log)); return scale; }; scale.base = function(_) { if (!arguments.length) return base; base = +_; linear.domain(domain.map(log)); return scale; }; scale.nice = function() { var niced = d3_scale_nice(domain.map(log), positive ? Math : d3_scale_logNiceNegative); linear.domain(niced); domain = niced.map(pow); return scale; }; scale.ticks = function() { var extent = d3_scaleExtent(domain), ticks = [], u = extent[0], v = extent[1], i = Math.floor(log(u)), j = Math.ceil(log(v)), n = base % 1 ? 2 : base; if (isFinite(j - i)) { if (positive) { for (;i < j; i++) for (var k = 1; k < n; k++) ticks.push(pow(i) * k); ticks.push(pow(i)); } else { ticks.push(pow(i)); for (;i++ < j; ) for (var k = n - 1; k > 0; k--) ticks.push(pow(i) * k); } for (i = 0; ticks[i] < u; i++) {} for (j = ticks.length; ticks[j - 1] > v; j--) {} ticks = ticks.slice(i, j); } return ticks; }; scale.tickFormat = function(n, format) { if (!arguments.length) return d3_scale_logFormat; if (arguments.length < 2) format = d3_scale_logFormat; else if (typeof format !== "function") format = d3.format(format); var k = Math.max(1, base * n / scale.ticks().length); return function(d) { var i = d / pow(Math.round(log(d))); if (i * base < base - .5) i *= base; return i <= k ? format(d) : ""; }; }; scale.copy = function() { return d3_scale_log(linear.copy(), base, positive, domain); }; return d3_scale_linearRebind(scale, linear); } var d3_scale_logFormat = d3.format(".0e"), d3_scale_logNiceNegative = { floor: function(x) { return -Math.ceil(-x); }, ceil: function(x) { return -Math.floor(-x); } }; d3.scale.pow = function() { return d3_scale_pow(d3.scale.linear(), 1, [ 0, 1 ]); }; function d3_scale_pow(linear, exponent, domain) { var powp = d3_scale_powPow(exponent), powb = d3_scale_powPow(1 / exponent); function scale(x) { return linear(powp(x)); } scale.invert = function(x) { return powb(linear.invert(x)); }; scale.domain = function(x) { if (!arguments.length) return domain; linear.domain((domain = x.map(Number)).map(powp)); return scale; }; scale.ticks = function(m) { return d3_scale_linearTicks(domain, m); }; scale.tickFormat = function(m, format) { return d3_scale_linearTickFormat(domain, m, format); }; scale.nice = function(m) { return scale.domain(d3_scale_linearNice(domain, m)); }; scale.exponent = function(x) { if (!arguments.length) return exponent; powp = d3_scale_powPow(exponent = x); powb = d3_scale_powPow(1 / exponent); linear.domain(domain.map(powp)); return scale; }; scale.copy = function() { return d3_scale_pow(linear.copy(), exponent, domain); }; return d3_scale_linearRebind(scale, linear); } function d3_scale_powPow(e) { return function(x) { return x < 0 ? -Math.pow(-x, e) : Math.pow(x, e); }; } d3.scale.sqrt = function() { return d3.scale.pow().exponent(.5); }; d3.scale.ordinal = function() { return d3_scale_ordinal([], { t: "range", a: [ [] ] }); }; function d3_scale_ordinal(domain, ranger) { var index, range, rangeBand; function scale(x) { return range[((index.get(x) || (ranger.t === "range" ? index.set(x, domain.push(x)) : NaN)) - 1) % range.length]; } function steps(start, step) { return d3.range(domain.length).map(function(i) { return start + step * i; }); } scale.domain = function(x) { if (!arguments.length) return domain; domain = []; index = new d3_Map(); var i = -1, n = x.length, xi; while (++i < n) if (!index.has(xi = x[i])) index.set(xi, domain.push(xi)); return scale[ranger.t].apply(scale, ranger.a); }; scale.range = function(x) { if (!arguments.length) return range; range = x; rangeBand = 0; ranger = { t: "range", a: arguments }; return scale; }; scale.rangePoints = function(x, padding) { if (arguments.length < 2) padding = 0; var start = x[0], stop = x[1], step = domain.length < 2 ? (start = (start + stop) / 2, 0) : (stop - start) / (domain.length - 1 + padding); range = steps(start + step * padding / 2, step); rangeBand = 0; ranger = { t: "rangePoints", a: arguments }; return scale; }; scale.rangeRoundPoints = function(x, padding) { if (arguments.length < 2) padding = 0; var start = x[0], stop = x[1], step = domain.length < 2 ? (start = stop = Math.round((start + stop) / 2), 0) : (stop - start) / (domain.length - 1 + padding) | 0; range = steps(start + Math.round(step * padding / 2 + (stop - start - (domain.length - 1 + padding) * step) / 2), step); rangeBand = 0; ranger = { t: "rangeRoundPoints", a: arguments }; return scale; }; scale.rangeBands = function(x, padding, outerPadding) { if (arguments.length < 2) padding = 0; if (arguments.length < 3) outerPadding = padding; var reverse = x[1] < x[0], start = x[reverse - 0], stop = x[1 - reverse], step = (stop - start) / (domain.length - padding + 2 * outerPadding); range = steps(start + step * outerPadding, step); if (reverse) range.reverse(); rangeBand = step * (1 - padding); ranger = { t: "rangeBands", a: arguments }; return scale; }; scale.rangeRoundBands = function(x, padding, outerPadding) { if (arguments.length < 2) padding = 0; if (arguments.length < 3) outerPadding = padding; var reverse = x[1] < x[0], start = x[reverse - 0], stop = x[1 - reverse], step = Math.floor((stop - start) / (domain.length - padding + 2 * outerPadding)); range = steps(start + Math.round((stop - start - (domain.length - padding) * step) / 2), step); if (reverse) range.reverse(); rangeBand = Math.round(step * (1 - padding)); ranger = { t: "rangeRoundBands", a: arguments }; return scale; }; scale.rangeBand = function() { return rangeBand; }; scale.rangeExtent = function() { return d3_scaleExtent(ranger.a[0]); }; scale.copy = function() { return d3_scale_ordinal(domain, ranger); }; return scale.domain(domain); } d3.scale.category10 = function() { return d3.scale.ordinal().range(d3_category10); }; d3.scale.category20 = function() { return d3.scale.ordinal().range(d3_category20); }; d3.scale.category20b = function() { return d3.scale.ordinal().range(d3_category20b); }; d3.scale.category20c = function() { return d3.scale.ordinal().range(d3_category20c); }; var d3_category10 = [ 2062260, 16744206, 2924588, 14034728, 9725885, 9197131, 14907330, 8355711, 12369186, 1556175 ].map(d3_rgbString); var d3_category20 = [ 2062260, 11454440, 16744206, 16759672, 2924588, 10018698, 14034728, 16750742, 9725885, 12955861, 9197131, 12885140, 14907330, 16234194, 8355711, 13092807, 12369186, 14408589, 1556175, 10410725 ].map(d3_rgbString); var d3_category20b = [ 3750777, 5395619, 7040719, 10264286, 6519097, 9216594, 11915115, 13556636, 9202993, 12426809, 15186514, 15190932, 8666169, 11356490, 14049643, 15177372, 8077683, 10834324, 13528509, 14589654 ].map(d3_rgbString); var d3_category20c = [ 3244733, 7057110, 10406625, 13032431, 15095053, 16616764, 16625259, 16634018, 3253076, 7652470, 10607003, 13101504, 7695281, 10394312, 12369372, 14342891, 6513507, 9868950, 12434877, 14277081 ].map(d3_rgbString); d3.scale.quantile = function() { return d3_scale_quantile([], []); }; function d3_scale_quantile(domain, range) { var thresholds; function rescale() { var k = 0, q = range.length; thresholds = []; while (++k < q) thresholds[k - 1] = d3.quantile(domain, k / q); return scale; } function scale(x) { if (!isNaN(x = +x)) return range[d3.bisect(thresholds, x)]; } scale.domain = function(x) { if (!arguments.length) return domain; domain = x.map(d3_number).filter(d3_numeric).sort(d3_ascending); return rescale(); }; scale.range = function(x) { if (!arguments.length) return range; range = x; return rescale(); }; scale.quantiles = function() { return thresholds; }; scale.invertExtent = function(y) { y = range.indexOf(y); return y < 0 ? [ NaN, NaN ] : [ y > 0 ? thresholds[y - 1] : domain[0], y < thresholds.length ? thresholds[y] : domain[domain.length - 1] ]; }; scale.copy = function() { return d3_scale_quantile(domain, range); }; return rescale(); } d3.scale.quantize = function() { return d3_scale_quantize(0, 1, [ 0, 1 ]); }; function d3_scale_quantize(x0, x1, range) { var kx, i; function scale(x) { return range[Math.max(0, Math.min(i, Math.floor(kx * (x - x0))))]; } function rescale() { kx = range.length / (x1 - x0); i = range.length - 1; return scale; } scale.domain = function(x) { if (!arguments.length) return [ x0, x1 ]; x0 = +x[0]; x1 = +x[x.length - 1]; return rescale(); }; scale.range = function(x) { if (!arguments.length) return range; range = x; return rescale(); }; scale.invertExtent = function(y) { y = range.indexOf(y); y = y < 0 ? NaN : y / kx + x0; return [ y, y + 1 / kx ]; }; scale.copy = function() { return d3_scale_quantize(x0, x1, range); }; return rescale(); } d3.scale.threshold = function() { return d3_scale_threshold([ .5 ], [ 0, 1 ]); }; function d3_scale_threshold(domain, range) { function scale(x) { if (x <= x) return range[d3.bisect(domain, x)]; } scale.domain = function(_) { if (!arguments.length) return domain; domain = _; return scale; }; scale.range = function(_) { if (!arguments.length) return range; range = _; return scale; }; scale.invertExtent = function(y) { y = range.indexOf(y); return [ domain[y - 1], domain[y] ]; }; scale.copy = function() { return d3_scale_threshold(domain, range); }; return scale; } d3.scale.identity = function() { return d3_scale_identity([ 0, 1 ]); }; function d3_scale_identity(domain) { function identity(x) { return +x; } identity.invert = identity; identity.domain = identity.range = function(x) { if (!arguments.length) return domain; domain = x.map(identity); return identity; }; identity.ticks = function(m) { return d3_scale_linearTicks(domain, m); }; identity.tickFormat = function(m, format) { return d3_scale_linearTickFormat(domain, m, format); }; identity.copy = function() { return d3_scale_identity(domain); }; return identity; } d3.svg = {}; function d3_zero() { return 0; } d3.svg.arc = function() { var innerRadius = d3_svg_arcInnerRadius, outerRadius = d3_svg_arcOuterRadius, cornerRadius = d3_zero, padRadius = d3_svg_arcAuto, startAngle = d3_svg_arcStartAngle, endAngle = d3_svg_arcEndAngle, padAngle = d3_svg_arcPadAngle; function arc() { var r0 = Math.max(0, +innerRadius.apply(this, arguments)), r1 = Math.max(0, +outerRadius.apply(this, arguments)), a0 = startAngle.apply(this, arguments) - halfπ, a1 = endAngle.apply(this, arguments) - halfπ, da = Math.abs(a1 - a0), cw = a0 > a1 ? 0 : 1; if (r1 < r0) rc = r1, r1 = r0, r0 = rc; if (da >= τε) return circleSegment(r1, cw) + (r0 ? circleSegment(r0, 1 - cw) : "") + "Z"; var rc, cr, rp, ap, p0 = 0, p1 = 0, x0, y0, x1, y1, x2, y2, x3, y3, path = []; if (ap = (+padAngle.apply(this, arguments) || 0) / 2) { rp = padRadius === d3_svg_arcAuto ? Math.sqrt(r0 * r0 + r1 * r1) : +padRadius.apply(this, arguments); if (!cw) p1 *= -1; if (r1) p1 = d3_asin(rp / r1 * Math.sin(ap)); if (r0) p0 = d3_asin(rp / r0 * Math.sin(ap)); } if (r1) { x0 = r1 * Math.cos(a0 + p1); y0 = r1 * Math.sin(a0 + p1); x1 = r1 * Math.cos(a1 - p1); y1 = r1 * Math.sin(a1 - p1); var l1 = Math.abs(a1 - a0 - 2 * p1) <= π ? 0 : 1; if (p1 && d3_svg_arcSweep(x0, y0, x1, y1) === cw ^ l1) { var h1 = (a0 + a1) / 2; x0 = r1 * Math.cos(h1); y0 = r1 * Math.sin(h1); x1 = y1 = null; } } else { x0 = y0 = 0; } if (r0) { x2 = r0 * Math.cos(a1 - p0); y2 = r0 * Math.sin(a1 - p0); x3 = r0 * Math.cos(a0 + p0); y3 = r0 * Math.sin(a0 + p0); var l0 = Math.abs(a0 - a1 + 2 * p0) <= π ? 0 : 1; if (p0 && d3_svg_arcSweep(x2, y2, x3, y3) === 1 - cw ^ l0) { var h0 = (a0 + a1) / 2; x2 = r0 * Math.cos(h0); y2 = r0 * Math.sin(h0); x3 = y3 = null; } } else { x2 = y2 = 0; } if (da > ε && (rc = Math.min(Math.abs(r1 - r0) / 2, +cornerRadius.apply(this, arguments))) > .001) { cr = r0 < r1 ^ cw ? 0 : 1; var rc1 = rc, rc0 = rc; if (da < π) { var oc = x3 == null ? [ x2, y2 ] : x1 == null ? [ x0, y0 ] : d3_geom_polygonIntersect([ x0, y0 ], [ x3, y3 ], [ x1, y1 ], [ x2, y2 ]), ax = x0 - oc[0], ay = y0 - oc[1], bx = x1 - oc[0], by = y1 - oc[1], kc = 1 / Math.sin(Math.acos((ax * bx + ay * by) / (Math.sqrt(ax * ax + ay * ay) * Math.sqrt(bx * bx + by * by))) / 2), lc = Math.sqrt(oc[0] * oc[0] + oc[1] * oc[1]); rc0 = Math.min(rc, (r0 - lc) / (kc - 1)); rc1 = Math.min(rc, (r1 - lc) / (kc + 1)); } if (x1 != null) { var t30 = d3_svg_arcCornerTangents(x3 == null ? [ x2, y2 ] : [ x3, y3 ], [ x0, y0 ], r1, rc1, cw), t12 = d3_svg_arcCornerTangents([ x1, y1 ], [ x2, y2 ], r1, rc1, cw); if (rc === rc1) { path.push("M", t30[0], "A", rc1, ",", rc1, " 0 0,", cr, " ", t30[1], "A", r1, ",", r1, " 0 ", 1 - cw ^ d3_svg_arcSweep(t30[1][0], t30[1][1], t12[1][0], t12[1][1]), ",", cw, " ", t12[1], "A", rc1, ",", rc1, " 0 0,", cr, " ", t12[0]); } else { path.push("M", t30[0], "A", rc1, ",", rc1, " 0 1,", cr, " ", t12[0]); } } else { path.push("M", x0, ",", y0); } if (x3 != null) { var t03 = d3_svg_arcCornerTangents([ x0, y0 ], [ x3, y3 ], r0, -rc0, cw), t21 = d3_svg_arcCornerTangents([ x2, y2 ], x1 == null ? [ x0, y0 ] : [ x1, y1 ], r0, -rc0, cw); if (rc === rc0) { path.push("L", t21[0], "A", rc0, ",", rc0, " 0 0,", cr, " ", t21[1], "A", r0, ",", r0, " 0 ", cw ^ d3_svg_arcSweep(t21[1][0], t21[1][1], t03[1][0], t03[1][1]), ",", 1 - cw, " ", t03[1], "A", rc0, ",", rc0, " 0 0,", cr, " ", t03[0]); } else { path.push("L", t21[0], "A", rc0, ",", rc0, " 0 0,", cr, " ", t03[0]); } } else { path.push("L", x2, ",", y2); } } else { path.push("M", x0, ",", y0); if (x1 != null) path.push("A", r1, ",", r1, " 0 ", l1, ",", cw, " ", x1, ",", y1); path.push("L", x2, ",", y2); if (x3 != null) path.push("A", r0, ",", r0, " 0 ", l0, ",", 1 - cw, " ", x3, ",", y3); } path.push("Z"); return path.join(""); } function circleSegment(r1, cw) { return "M0," + r1 + "A" + r1 + "," + r1 + " 0 1," + cw + " 0," + -r1 + "A" + r1 + "," + r1 + " 0 1," + cw + " 0," + r1; } arc.innerRadius = function(v) { if (!arguments.length) return innerRadius; innerRadius = d3_functor(v); return arc; }; arc.outerRadius = function(v) { if (!arguments.length) return outerRadius; outerRadius = d3_functor(v); return arc; }; arc.cornerRadius = function(v) { if (!arguments.length) return cornerRadius; cornerRadius = d3_functor(v); return arc; }; arc.padRadius = function(v) { if (!arguments.length) return padRadius; padRadius = v == d3_svg_arcAuto ? d3_svg_arcAuto : d3_functor(v); return arc; }; arc.startAngle = function(v) { if (!arguments.length) return startAngle; startAngle = d3_functor(v); return arc; }; arc.endAngle = function(v) { if (!arguments.length) return endAngle; endAngle = d3_functor(v); return arc; }; arc.padAngle = function(v) { if (!arguments.length) return padAngle; padAngle = d3_functor(v); return arc; }; arc.centroid = function() { var r = (+innerRadius.apply(this, arguments) + +outerRadius.apply(this, arguments)) / 2, a = (+startAngle.apply(this, arguments) + +endAngle.apply(this, arguments)) / 2 - halfπ; return [ Math.cos(a) * r, Math.sin(a) * r ]; }; return arc; }; var d3_svg_arcAuto = "auto"; function d3_svg_arcInnerRadius(d) { return d.innerRadius; } function d3_svg_arcOuterRadius(d) { return d.outerRadius; } function d3_svg_arcStartAngle(d) { return d.startAngle; } function d3_svg_arcEndAngle(d) { return d.endAngle; } function d3_svg_arcPadAngle(d) { return d && d.padAngle; } function d3_svg_arcSweep(x0, y0, x1, y1) { return (x0 - x1) * y0 - (y0 - y1) * x0 > 0 ? 0 : 1; } function d3_svg_arcCornerTangents(p0, p1, r1, rc, cw) { var x01 = p0[0] - p1[0], y01 = p0[1] - p1[1], lo = (cw ? rc : -rc) / Math.sqrt(x01 * x01 + y01 * y01), ox = lo * y01, oy = -lo * x01, x1 = p0[0] + ox, y1 = p0[1] + oy, x2 = p1[0] + ox, y2 = p1[1] + oy, x3 = (x1 + x2) / 2, y3 = (y1 + y2) / 2, dx = x2 - x1, dy = y2 - y1, d2 = dx * dx + dy * dy, r = r1 - rc, D = x1 * y2 - x2 * y1, d = (dy < 0 ? -1 : 1) * Math.sqrt(Math.max(0, r * r * d2 - D * D)), cx0 = (D * dy - dx * d) / d2, cy0 = (-D * dx - dy * d) / d2, cx1 = (D * dy + dx * d) / d2, cy1 = (-D * dx + dy * d) / d2, dx0 = cx0 - x3, dy0 = cy0 - y3, dx1 = cx1 - x3, dy1 = cy1 - y3; if (dx0 * dx0 + dy0 * dy0 > dx1 * dx1 + dy1 * dy1) cx0 = cx1, cy0 = cy1; return [ [ cx0 - ox, cy0 - oy ], [ cx0 * r1 / r, cy0 * r1 / r ] ]; } function d3_svg_line(projection) { var x = d3_geom_pointX, y = d3_geom_pointY, defined = d3_true, interpolate = d3_svg_lineLinear, interpolateKey = interpolate.key, tension = .7; function line(data) { var segments = [], points = [], i = -1, n = data.length, d, fx = d3_functor(x), fy = d3_functor(y); function segment() { segments.push("M", interpolate(projection(points), tension)); } while (++i < n) { if (defined.call(this, d = data[i], i)) { points.push([ +fx.call(this, d, i), +fy.call(this, d, i) ]); } else if (points.length) { segment(); points = []; } } if (points.length) segment(); return segments.length ? segments.join("") : null; } line.x = function(_) { if (!arguments.length) return x; x = _; return line; }; line.y = function(_) { if (!arguments.length) return y; y = _; return line; }; line.defined = function(_) { if (!arguments.length) return defined; defined = _; return line; }; line.interpolate = function(_) { if (!arguments.length) return interpolateKey; if (typeof _ === "function") interpolateKey = interpolate = _; else interpolateKey = (interpolate = d3_svg_lineInterpolators.get(_) || d3_svg_lineLinear).key; return line; }; line.tension = function(_) { if (!arguments.length) return tension; tension = _; return line; }; return line; } d3.svg.line = function() { return d3_svg_line(d3_identity); }; var d3_svg_lineInterpolators = d3.map({ linear: d3_svg_lineLinear, "linear-closed": d3_svg_lineLinearClosed, step: d3_svg_lineStep, "step-before": d3_svg_lineStepBefore, "step-after": d3_svg_lineStepAfter, basis: d3_svg_lineBasis, "basis-open": d3_svg_lineBasisOpen, "basis-closed": d3_svg_lineBasisClosed, bundle: d3_svg_lineBundle, cardinal: d3_svg_lineCardinal, "cardinal-open": d3_svg_lineCardinalOpen, "cardinal-closed": d3_svg_lineCardinalClosed, monotone: d3_svg_lineMonotone }); d3_svg_lineInterpolators.forEach(function(key, value) { value.key = key; value.closed = /-closed$/.test(key); }); function d3_svg_lineLinear(points) { return points.length > 1 ? points.join("L") : points + "Z"; } function d3_svg_lineLinearClosed(points) { return points.join("L") + "Z"; } function d3_svg_lineStep(points) { var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ]; while (++i < n) path.push("H", (p[0] + (p = points[i])[0]) / 2, "V", p[1]); if (n > 1) path.push("H", p[0]); return path.join(""); } function d3_svg_lineStepBefore(points) { var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ]; while (++i < n) path.push("V", (p = points[i])[1], "H", p[0]); return path.join(""); } function d3_svg_lineStepAfter(points) { var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ]; while (++i < n) path.push("H", (p = points[i])[0], "V", p[1]); return path.join(""); } function d3_svg_lineCardinalOpen(points, tension) { return points.length < 4 ? d3_svg_lineLinear(points) : points[1] + d3_svg_lineHermite(points.slice(1, -1), d3_svg_lineCardinalTangents(points, tension)); } function d3_svg_lineCardinalClosed(points, tension) { return points.length < 3 ? d3_svg_lineLinearClosed(points) : points[0] + d3_svg_lineHermite((points.push(points[0]), points), d3_svg_lineCardinalTangents([ points[points.length - 2] ].concat(points, [ points[1] ]), tension)); } function d3_svg_lineCardinal(points, tension) { return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite(points, d3_svg_lineCardinalTangents(points, tension)); } function d3_svg_lineHermite(points, tangents) { if (tangents.length < 1 || points.length != tangents.length && points.length != tangents.length + 2) { return d3_svg_lineLinear(points); } var quad = points.length != tangents.length, path = "", p0 = points[0], p = points[1], t0 = tangents[0], t = t0, pi = 1; if (quad) { path += "Q" + (p[0] - t0[0] * 2 / 3) + "," + (p[1] - t0[1] * 2 / 3) + "," + p[0] + "," + p[1]; p0 = points[1]; pi = 2; } if (tangents.length > 1) { t = tangents[1]; p = points[pi]; pi++; path += "C" + (p0[0] + t0[0]) + "," + (p0[1] + t0[1]) + "," + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1]; for (var i = 2; i < tangents.length; i++, pi++) { p = points[pi]; t = tangents[i]; path += "S" + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1]; } } if (quad) { var lp = points[pi]; path += "Q" + (p[0] + t[0] * 2 / 3) + "," + (p[1] + t[1] * 2 / 3) + "," + lp[0] + "," + lp[1]; } return path; } function d3_svg_lineCardinalTangents(points, tension) { var tangents = [], a = (1 - tension) / 2, p0, p1 = points[0], p2 = points[1], i = 1, n = points.length; while (++i < n) { p0 = p1; p1 = p2; p2 = points[i]; tangents.push([ a * (p2[0] - p0[0]), a * (p2[1] - p0[1]) ]); } return tangents; } function d3_svg_lineBasis(points) { if (points.length < 3) return d3_svg_lineLinear(points); var i = 1, n = points.length, pi = points[0], x0 = pi[0], y0 = pi[1], px = [ x0, x0, x0, (pi = points[1])[0] ], py = [ y0, y0, y0, pi[1] ], path = [ x0, ",", y0, "L", d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py) ]; points.push(points[n - 1]); while (++i <= n) { pi = points[i]; px.shift(); px.push(pi[0]); py.shift(); py.push(pi[1]); d3_svg_lineBasisBezier(path, px, py); } points.pop(); path.push("L", pi); return path.join(""); } function d3_svg_lineBasisOpen(points) { if (points.length < 4) return d3_svg_lineLinear(points); var path = [], i = -1, n = points.length, pi, px = [ 0 ], py = [ 0 ]; while (++i < 3) { pi = points[i]; px.push(pi[0]); py.push(pi[1]); } path.push(d3_svg_lineDot4(d3_svg_lineBasisBezier3, px) + "," + d3_svg_lineDot4(d3_svg_lineBasisBezier3, py)); --i; while (++i < n) { pi = points[i]; px.shift(); px.push(pi[0]); py.shift(); py.push(pi[1]); d3_svg_lineBasisBezier(path, px, py); } return path.join(""); } function d3_svg_lineBasisClosed(points) { var path, i = -1, n = points.length, m = n + 4, pi, px = [], py = []; while (++i < 4) { pi = points[i % n]; px.push(pi[0]); py.push(pi[1]); } path = [ d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py) ]; --i; while (++i < m) { pi = points[i % n]; px.shift(); px.push(pi[0]); py.shift(); py.push(pi[1]); d3_svg_lineBasisBezier(path, px, py); } return path.join(""); } function d3_svg_lineBundle(points, tension) { var n = points.length - 1; if (n) { var x0 = points[0][0], y0 = points[0][1], dx = points[n][0] - x0, dy = points[n][1] - y0, i = -1, p, t; while (++i <= n) { p = points[i]; t = i / n; p[0] = tension * p[0] + (1 - tension) * (x0 + t * dx); p[1] = tension * p[1] + (1 - tension) * (y0 + t * dy); } } return d3_svg_lineBasis(points); } function d3_svg_lineDot4(a, b) { return a[0] * b[0] + a[1] * b[1] + a[2] * b[2] + a[3] * b[3]; } var d3_svg_lineBasisBezier1 = [ 0, 2 / 3, 1 / 3, 0 ], d3_svg_lineBasisBezier2 = [ 0, 1 / 3, 2 / 3, 0 ], d3_svg_lineBasisBezier3 = [ 0, 1 / 6, 2 / 3, 1 / 6 ]; function d3_svg_lineBasisBezier(path, x, y) { path.push("C", d3_svg_lineDot4(d3_svg_lineBasisBezier1, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier1, y), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier2, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier2, y), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, y)); } function d3_svg_lineSlope(p0, p1) { return (p1[1] - p0[1]) / (p1[0] - p0[0]); } function d3_svg_lineFiniteDifferences(points) { var i = 0, j = points.length - 1, m = [], p0 = points[0], p1 = points[1], d = m[0] = d3_svg_lineSlope(p0, p1); while (++i < j) { m[i] = (d + (d = d3_svg_lineSlope(p0 = p1, p1 = points[i + 1]))) / 2; } m[i] = d; return m; } function d3_svg_lineMonotoneTangents(points) { var tangents = [], d, a, b, s, m = d3_svg_lineFiniteDifferences(points), i = -1, j = points.length - 1; while (++i < j) { d = d3_svg_lineSlope(points[i], points[i + 1]); if (abs(d) < ε) { m[i] = m[i + 1] = 0; } else { a = m[i] / d; b = m[i + 1] / d; s = a * a + b * b; if (s > 9) { s = d * 3 / Math.sqrt(s); m[i] = s * a; m[i + 1] = s * b; } } } i = -1; while (++i <= j) { s = (points[Math.min(j, i + 1)][0] - points[Math.max(0, i - 1)][0]) / (6 * (1 + m[i] * m[i])); tangents.push([ s || 0, m[i] * s || 0 ]); } return tangents; } function d3_svg_lineMonotone(points) { return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite(points, d3_svg_lineMonotoneTangents(points)); } d3.svg.line.radial = function() { var line = d3_svg_line(d3_svg_lineRadial); line.radius = line.x, delete line.x; line.angle = line.y, delete line.y; return line; }; function d3_svg_lineRadial(points) { var point, i = -1, n = points.length, r, a; while (++i < n) { point = points[i]; r = point[0]; a = point[1] - halfπ; point[0] = r * Math.cos(a); point[1] = r * Math.sin(a); } return points; } function d3_svg_area(projection) { var x0 = d3_geom_pointX, x1 = d3_geom_pointX, y0 = 0, y1 = d3_geom_pointY, defined = d3_true, interpolate = d3_svg_lineLinear, interpolateKey = interpolate.key, interpolateReverse = interpolate, L = "L", tension = .7; function area(data) { var segments = [], points0 = [], points1 = [], i = -1, n = data.length, d, fx0 = d3_functor(x0), fy0 = d3_functor(y0), fx1 = x0 === x1 ? function() { return x; } : d3_functor(x1), fy1 = y0 === y1 ? function() { return y; } : d3_functor(y1), x, y; function segment() { segments.push("M", interpolate(projection(points1), tension), L, interpolateReverse(projection(points0.reverse()), tension), "Z"); } while (++i < n) { if (defined.call(this, d = data[i], i)) { points0.push([ x = +fx0.call(this, d, i), y = +fy0.call(this, d, i) ]); points1.push([ +fx1.call(this, d, i), +fy1.call(this, d, i) ]); } else if (points0.length) { segment(); points0 = []; points1 = []; } } if (points0.length) segment(); return segments.length ? segments.join("") : null; } area.x = function(_) { if (!arguments.length) return x1; x0 = x1 = _; return area; }; area.x0 = function(_) { if (!arguments.length) return x0; x0 = _; return area; }; area.x1 = function(_) { if (!arguments.length) return x1; x1 = _; return area; }; area.y = function(_) { if (!arguments.length) return y1; y0 = y1 = _; return area; }; area.y0 = function(_) { if (!arguments.length) return y0; y0 = _; return area; }; area.y1 = function(_) { if (!arguments.length) return y1; y1 = _; return area; }; area.defined = function(_) { if (!arguments.length) return defined; defined = _; return area; }; area.interpolate = function(_) { if (!arguments.length) return interpolateKey; if (typeof _ === "function") interpolateKey = interpolate = _; else interpolateKey = (interpolate = d3_svg_lineInterpolators.get(_) || d3_svg_lineLinear).key; interpolateReverse = interpolate.reverse || interpolate; L = interpolate.closed ? "M" : "L"; return area; }; area.tension = function(_) { if (!arguments.length) return tension; tension = _; return area; }; return area; } d3_svg_lineStepBefore.reverse = d3_svg_lineStepAfter; d3_svg_lineStepAfter.reverse = d3_svg_lineStepBefore; d3.svg.area = function() { return d3_svg_area(d3_identity); }; d3.svg.area.radial = function() { var area = d3_svg_area(d3_svg_lineRadial); area.radius = area.x, delete area.x; area.innerRadius = area.x0, delete area.x0; area.outerRadius = area.x1, delete area.x1; area.angle = area.y, delete area.y; area.startAngle = area.y0, delete area.y0; area.endAngle = area.y1, delete area.y1; return area; }; d3.svg.chord = function() { var source = d3_source, target = d3_target, radius = d3_svg_chordRadius, startAngle = d3_svg_arcStartAngle, endAngle = d3_svg_arcEndAngle; function chord(d, i) { var s = subgroup(this, source, d, i), t = subgroup(this, target, d, i); return "M" + s.p0 + arc(s.r, s.p1, s.a1 - s.a0) + (equals(s, t) ? curve(s.r, s.p1, s.r, s.p0) : curve(s.r, s.p1, t.r, t.p0) + arc(t.r, t.p1, t.a1 - t.a0) + curve(t.r, t.p1, s.r, s.p0)) + "Z"; } function subgroup(self, f, d, i) { var subgroup = f.call(self, d, i), r = radius.call(self, subgroup, i), a0 = startAngle.call(self, subgroup, i) - halfπ, a1 = endAngle.call(self, subgroup, i) - halfπ; return { r: r, a0: a0, a1: a1, p0: [ r * Math.cos(a0), r * Math.sin(a0) ], p1: [ r * Math.cos(a1), r * Math.sin(a1) ] }; } function equals(a, b) { return a.a0 == b.a0 && a.a1 == b.a1; } function arc(r, p, a) { return "A" + r + "," + r + " 0 " + +(a > π) + ",1 " + p; } function curve(r0, p0, r1, p1) { return "Q 0,0 " + p1; } chord.radius = function(v) { if (!arguments.length) return radius; radius = d3_functor(v); return chord; }; chord.source = function(v) { if (!arguments.length) return source; source = d3_functor(v); return chord; }; chord.target = function(v) { if (!arguments.length) return target; target = d3_functor(v); return chord; }; chord.startAngle = function(v) { if (!arguments.length) return startAngle; startAngle = d3_functor(v); return chord; }; chord.endAngle = function(v) { if (!arguments.length) return endAngle; endAngle = d3_functor(v); return chord; }; return chord; }; function d3_svg_chordRadius(d) { return d.radius; } d3.svg.diagonal = function() { var source = d3_source, target = d3_target, projection = d3_svg_diagonalProjection; function diagonal(d, i) { var p0 = source.call(this, d, i), p3 = target.call(this, d, i), m = (p0.y + p3.y) / 2, p = [ p0, { x: p0.x, y: m }, { x: p3.x, y: m }, p3 ]; p = p.map(projection); return "M" + p[0] + "C" + p[1] + " " + p[2] + " " + p[3]; } diagonal.source = function(x) { if (!arguments.length) return source; source = d3_functor(x); return diagonal; }; diagonal.target = function(x) { if (!arguments.length) return target; target = d3_functor(x); return diagonal; }; diagonal.projection = function(x) { if (!arguments.length) return projection; projection = x; return diagonal; }; return diagonal; }; function d3_svg_diagonalProjection(d) { return [ d.x, d.y ]; } d3.svg.diagonal.radial = function() { var diagonal = d3.svg.diagonal(), projection = d3_svg_diagonalProjection, projection_ = diagonal.projection; diagonal.projection = function(x) { return arguments.length ? projection_(d3_svg_diagonalRadialProjection(projection = x)) : projection; }; return diagonal; }; function d3_svg_diagonalRadialProjection(projection) { return function() { var d = projection.apply(this, arguments), r = d[0], a = d[1] - halfπ; return [ r * Math.cos(a), r * Math.sin(a) ]; }; } d3.svg.symbol = function() { var type = d3_svg_symbolType, size = d3_svg_symbolSize; function symbol(d, i) { return (d3_svg_symbols.get(type.call(this, d, i)) || d3_svg_symbolCircle)(size.call(this, d, i)); } symbol.type = function(x) { if (!arguments.length) return type; type = d3_functor(x); return symbol; }; symbol.size = function(x) { if (!arguments.length) return size; size = d3_functor(x); return symbol; }; return symbol; }; function d3_svg_symbolSize() { return 64; } function d3_svg_symbolType() { return "circle"; } function d3_svg_symbolCircle(size) { var r = Math.sqrt(size / π); return "M0," + r + "A" + r + "," + r + " 0 1,1 0," + -r + "A" + r + "," + r + " 0 1,1 0," + r + "Z"; } var d3_svg_symbols = d3.map({ circle: d3_svg_symbolCircle, cross: function(size) { var r = Math.sqrt(size / 5) / 2; return "M" + -3 * r + "," + -r + "H" + -r + "V" + -3 * r + "H" + r + "V" + -r + "H" + 3 * r + "V" + r + "H" + r + "V" + 3 * r + "H" + -r + "V" + r + "H" + -3 * r + "Z"; }, diamond: function(size) { var ry = Math.sqrt(size / (2 * d3_svg_symbolTan30)), rx = ry * d3_svg_symbolTan30; return "M0," + -ry + "L" + rx + ",0" + " 0," + ry + " " + -rx + ",0" + "Z"; }, square: function(size) { var r = Math.sqrt(size) / 2; return "M" + -r + "," + -r + "L" + r + "," + -r + " " + r + "," + r + " " + -r + "," + r + "Z"; }, "triangle-down": function(size) { var rx = Math.sqrt(size / d3_svg_symbolSqrt3), ry = rx * d3_svg_symbolSqrt3 / 2; return "M0," + ry + "L" + rx + "," + -ry + " " + -rx + "," + -ry + "Z"; }, "triangle-up": function(size) { var rx = Math.sqrt(size / d3_svg_symbolSqrt3), ry = rx * d3_svg_symbolSqrt3 / 2; return "M0," + -ry + "L" + rx + "," + ry + " " + -rx + "," + ry + "Z"; } }); d3.svg.symbolTypes = d3_svg_symbols.keys(); var d3_svg_symbolSqrt3 = Math.sqrt(3), d3_svg_symbolTan30 = Math.tan(30 * d3_radians); d3_selectionPrototype.transition = function(name) { var id = d3_transitionInheritId || ++d3_transitionId, ns = d3_transitionNamespace(name), subgroups = [], subgroup, node, transition = d3_transitionInherit || { time: Date.now(), ease: d3_ease_cubicInOut, delay: 0, duration: 250 }; for (var j = -1, m = this.length; ++j < m; ) { subgroups.push(subgroup = []); for (var group = this[j], i = -1, n = group.length; ++i < n; ) { if (node = group[i]) d3_transitionNode(node, i, ns, id, transition); subgroup.push(node); } } return d3_transition(subgroups, ns, id); }; d3_selectionPrototype.interrupt = function(name) { return this.each(name == null ? d3_selection_interrupt : d3_selection_interruptNS(d3_transitionNamespace(name))); }; var d3_selection_interrupt = d3_selection_interruptNS(d3_transitionNamespace()); function d3_selection_interruptNS(ns) { return function() { var lock, activeId, active; if ((lock = this[ns]) && (active = lock[activeId = lock.active])) { active.timer.c = null; active.timer.t = NaN; if (--lock.count) delete lock[activeId]; else delete this[ns]; lock.active += .5; active.event && active.event.interrupt.call(this, this.__data__, active.index); } }; } function d3_transition(groups, ns, id) { d3_subclass(groups, d3_transitionPrototype); groups.namespace = ns; groups.id = id; return groups; } var d3_transitionPrototype = [], d3_transitionId = 0, d3_transitionInheritId, d3_transitionInherit; d3_transitionPrototype.call = d3_selectionPrototype.call; d3_transitionPrototype.empty = d3_selectionPrototype.empty; d3_transitionPrototype.node = d3_selectionPrototype.node; d3_transitionPrototype.size = d3_selectionPrototype.size; d3.transition = function(selection, name) { return selection && selection.transition ? d3_transitionInheritId ? selection.transition(name) : selection : d3.selection().transition(selection); }; d3.transition.prototype = d3_transitionPrototype; d3_transitionPrototype.select = function(selector) { var id = this.id, ns = this.namespace, subgroups = [], subgroup, subnode, node; selector = d3_selection_selector(selector); for (var j = -1, m = this.length; ++j < m; ) { subgroups.push(subgroup = []); for (var group = this[j], i = -1, n = group.length; ++i < n; ) { if ((node = group[i]) && (subnode = selector.call(node, node.__data__, i, j))) { if ("__data__" in node) subnode.__data__ = node.__data__; d3_transitionNode(subnode, i, ns, id, node[ns][id]); subgroup.push(subnode); } else { subgroup.push(null); } } } return d3_transition(subgroups, ns, id); }; d3_transitionPrototype.selectAll = function(selector) { var id = this.id, ns = this.namespace, subgroups = [], subgroup, subnodes, node, subnode, transition; selector = d3_selection_selectorAll(selector); for (var j = -1, m = this.length; ++j < m; ) { for (var group = this[j], i = -1, n = group.length; ++i < n; ) { if (node = group[i]) { transition = node[ns][id]; subnodes = selector.call(node, node.__data__, i, j); subgroups.push(subgroup = []); for (var k = -1, o = subnodes.length; ++k < o; ) { if (subnode = subnodes[k]) d3_transitionNode(subnode, k, ns, id, transition); subgroup.push(subnode); } } } } return d3_transition(subgroups, ns, id); }; d3_transitionPrototype.filter = function(filter) { var subgroups = [], subgroup, group, node; if (typeof filter !== "function") filter = d3_selection_filter(filter); for (var j = 0, m = this.length; j < m; j++) { subgroups.push(subgroup = []); for (var group = this[j], i = 0, n = group.length; i < n; i++) { if ((node = group[i]) && filter.call(node, node.__data__, i, j)) { subgroup.push(node); } } } return d3_transition(subgroups, this.namespace, this.id); }; d3_transitionPrototype.tween = function(name, tween) { var id = this.id, ns = this.namespace; if (arguments.length < 2) return this.node()[ns][id].tween.get(name); return d3_selection_each(this, tween == null ? function(node) { node[ns][id].tween.remove(name); } : function(node) { node[ns][id].tween.set(name, tween); }); }; function d3_transition_tween(groups, name, value, tween) { var id = groups.id, ns = groups.namespace; return d3_selection_each(groups, typeof value === "function" ? function(node, i, j) { node[ns][id].tween.set(name, tween(value.call(node, node.__data__, i, j))); } : (value = tween(value), function(node) { node[ns][id].tween.set(name, value); })); } d3_transitionPrototype.attr = function(nameNS, value) { if (arguments.length < 2) { for (value in nameNS) this.attr(value, nameNS[value]); return this; } var interpolate = nameNS == "transform" ? d3_interpolateTransform : d3_interpolate, name = d3.ns.qualify(nameNS); function attrNull() { this.removeAttribute(name); } function attrNullNS() { this.removeAttributeNS(name.space, name.local); } function attrTween(b) { return b == null ? attrNull : (b += "", function() { var a = this.getAttribute(name), i; return a !== b && (i = interpolate(a, b), function(t) { this.setAttribute(name, i(t)); }); }); } function attrTweenNS(b) { return b == null ? attrNullNS : (b += "", function() { var a = this.getAttributeNS(name.space, name.local), i; return a !== b && (i = interpolate(a, b), function(t) { this.setAttributeNS(name.space, name.local, i(t)); }); }); } return d3_transition_tween(this, "attr." + nameNS, value, name.local ? attrTweenNS : attrTween); }; d3_transitionPrototype.attrTween = function(nameNS, tween) { var name = d3.ns.qualify(nameNS); function attrTween(d, i) { var f = tween.call(this, d, i, this.getAttribute(name)); return f && function(t) { this.setAttribute(name, f(t)); }; } function attrTweenNS(d, i) { var f = tween.call(this, d, i, this.getAttributeNS(name.space, name.local)); return f && function(t) { this.setAttributeNS(name.space, name.local, f(t)); }; } return this.tween("attr." + nameNS, name.local ? attrTweenNS : attrTween); }; d3_transitionPrototype.style = function(name, value, priority) { var n = arguments.length; if (n < 3) { if (typeof name !== "string") { if (n < 2) value = ""; for (priority in name) this.style(priority, name[priority], value); return this; } priority = ""; } function styleNull() { this.style.removeProperty(name); } function styleString(b) { return b == null ? styleNull : (b += "", function() { var a = d3_window(this).getComputedStyle(this, null).getPropertyValue(name), i; return a !== b && (i = d3_interpolate(a, b), function(t) { this.style.setProperty(name, i(t), priority); }); }); } return d3_transition_tween(this, "style." + name, value, styleString); }; d3_transitionPrototype.styleTween = function(name, tween, priority) { if (arguments.length < 3) priority = ""; function styleTween(d, i) { var f = tween.call(this, d, i, d3_window(this).getComputedStyle(this, null).getPropertyValue(name)); return f && function(t) { this.style.setProperty(name, f(t), priority); }; } return this.tween("style." + name, styleTween); }; d3_transitionPrototype.text = function(value) { return d3_transition_tween(this, "text", value, d3_transition_text); }; function d3_transition_text(b) { if (b == null) b = ""; return function() { this.textContent = b; }; } d3_transitionPrototype.remove = function() { var ns = this.namespace; return this.each("end.transition", function() { var p; if (this[ns].count < 2 && (p = this.parentNode)) p.removeChild(this); }); }; d3_transitionPrototype.ease = function(value) { var id = this.id, ns = this.namespace; if (arguments.length < 1) return this.node()[ns][id].ease; if (typeof value !== "function") value = d3.ease.apply(d3, arguments); return d3_selection_each(this, function(node) { node[ns][id].ease = value; }); }; d3_transitionPrototype.delay = function(value) { var id = this.id, ns = this.namespace; if (arguments.length < 1) return this.node()[ns][id].delay; return d3_selection_each(this, typeof value === "function" ? function(node, i, j) { node[ns][id].delay = +value.call(node, node.__data__, i, j); } : (value = +value, function(node) { node[ns][id].delay = value; })); }; d3_transitionPrototype.duration = function(value) { var id = this.id, ns = this.namespace; if (arguments.length < 1) return this.node()[ns][id].duration; return d3_selection_each(this, typeof value === "function" ? function(node, i, j) { node[ns][id].duration = Math.max(1, value.call(node, node.__data__, i, j)); } : (value = Math.max(1, value), function(node) { node[ns][id].duration = value; })); }; d3_transitionPrototype.each = function(type, listener) { var id = this.id, ns = this.namespace; if (arguments.length < 2) { var inherit = d3_transitionInherit, inheritId = d3_transitionInheritId; try { d3_transitionInheritId = id; d3_selection_each(this, function(node, i, j) { d3_transitionInherit = node[ns][id]; type.call(node, node.__data__, i, j); }); } finally { d3_transitionInherit = inherit; d3_transitionInheritId = inheritId; } } else { d3_selection_each(this, function(node) { var transition = node[ns][id]; (transition.event || (transition.event = d3.dispatch("start", "end", "interrupt"))).on(type, listener); }); } return this; }; d3_transitionPrototype.transition = function() { var id0 = this.id, id1 = ++d3_transitionId, ns = this.namespace, subgroups = [], subgroup, group, node, transition; for (var j = 0, m = this.length; j < m; j++) { subgroups.push(subgroup = []); for (var group = this[j], i = 0, n = group.length; i < n; i++) { if (node = group[i]) { transition = node[ns][id0]; d3_transitionNode(node, i, ns, id1, { time: transition.time, ease: transition.ease, delay: transition.delay + transition.duration, duration: transition.duration }); } subgroup.push(node); } } return d3_transition(subgroups, ns, id1); }; function d3_transitionNamespace(name) { return name == null ? "__transition__" : "__transition_" + name + "__"; } function d3_transitionNode(node, i, ns, id, inherit) { var lock = node[ns] || (node[ns] = { active: 0, count: 0 }), transition = lock[id], time, timer, duration, ease, tweens; function schedule(elapsed) { var delay = transition.delay; timer.t = delay + time; if (delay <= elapsed) return start(elapsed - delay); timer.c = start; } function start(elapsed) { var activeId = lock.active, active = lock[activeId]; if (active) { active.timer.c = null; active.timer.t = NaN; --lock.count; delete lock[activeId]; active.event && active.event.interrupt.call(node, node.__data__, active.index); } for (var cancelId in lock) { if (+cancelId < id) { var cancel = lock[cancelId]; cancel.timer.c = null; cancel.timer.t = NaN; --lock.count; delete lock[cancelId]; } } timer.c = tick; d3_timer(function() { if (timer.c && tick(elapsed || 1)) { timer.c = null; timer.t = NaN; } return 1; }, 0, time); lock.active = id; transition.event && transition.event.start.call(node, node.__data__, i); tweens = []; transition.tween.forEach(function(key, value) { if (value = value.call(node, node.__data__, i)) { tweens.push(value); } }); ease = transition.ease; duration = transition.duration; } function tick(elapsed) { var t = elapsed / duration, e = ease(t), n = tweens.length; while (n > 0) { tweens[--n].call(node, e); } if (t >= 1) { transition.event && transition.event.end.call(node, node.__data__, i); if (--lock.count) delete lock[id]; else delete node[ns]; return 1; } } if (!transition) { time = inherit.time; timer = d3_timer(schedule, 0, time); transition = lock[id] = { tween: new d3_Map(), time: time, timer: timer, delay: inherit.delay, duration: inherit.duration, ease: inherit.ease, index: i }; inherit = null; ++lock.count; } } d3.svg.axis = function() { var scale = d3.scale.linear(), orient = d3_svg_axisDefaultOrient, innerTickSize = 6, outerTickSize = 6, tickPadding = 3, tickArguments_ = [ 10 ], tickValues = null, tickFormat_; function axis(g) { g.each(function() { var g = d3.select(this); var scale0 = this.__chart__ || scale, scale1 = this.__chart__ = scale.copy(); var ticks = tickValues == null ? scale1.ticks ? scale1.ticks.apply(scale1, tickArguments_) : scale1.domain() : tickValues, tickFormat = tickFormat_ == null ? scale1.tickFormat ? scale1.tickFormat.apply(scale1, tickArguments_) : d3_identity : tickFormat_, tick = g.selectAll(".tick").data(ticks, scale1), tickEnter = tick.enter().insert("g", ".domain").attr("class", "tick").style("opacity", ε), tickExit = d3.transition(tick.exit()).style("opacity", ε).remove(), tickUpdate = d3.transition(tick.order()).style("opacity", 1), tickSpacing = Math.max(innerTickSize, 0) + tickPadding, tickTransform; var range = d3_scaleRange(scale1), path = g.selectAll(".domain").data([ 0 ]), pathUpdate = (path.enter().append("path").attr("class", "domain"), d3.transition(path)); tickEnter.append("line"); tickEnter.append("text"); var lineEnter = tickEnter.select("line"), lineUpdate = tickUpdate.select("line"), text = tick.select("text").text(tickFormat), textEnter = tickEnter.select("text"), textUpdate = tickUpdate.select("text"), sign = orient === "top" || orient === "left" ? -1 : 1, x1, x2, y1, y2; if (orient === "bottom" || orient === "top") { tickTransform = d3_svg_axisX, x1 = "x", y1 = "y", x2 = "x2", y2 = "y2"; text.attr("dy", sign < 0 ? "0em" : ".71em").style("text-anchor", "middle"); pathUpdate.attr("d", "M" + range[0] + "," + sign * outerTickSize + "V0H" + range[1] + "V" + sign * outerTickSize); } else { tickTransform = d3_svg_axisY, x1 = "y", y1 = "x", x2 = "y2", y2 = "x2"; text.attr("dy", ".32em").style("text-anchor", sign < 0 ? "end" : "start"); pathUpdate.attr("d", "M" + sign * outerTickSize + "," + range[0] + "H0V" + range[1] + "H" + sign * outerTickSize); } lineEnter.attr(y2, sign * innerTickSize); textEnter.attr(y1, sign * tickSpacing); lineUpdate.attr(x2, 0).attr(y2, sign * innerTickSize); textUpdate.attr(x1, 0).attr(y1, sign * tickSpacing); if (scale1.rangeBand) { var x = scale1, dx = x.rangeBand() / 2; scale0 = scale1 = function(d) { return x(d) + dx; }; } else if (scale0.rangeBand) { scale0 = scale1; } else { tickExit.call(tickTransform, scale1, scale0); } tickEnter.call(tickTransform, scale0, scale1); tickUpdate.call(tickTransform, scale1, scale1); }); } axis.scale = function(x) { if (!arguments.length) return scale; scale = x; return axis; }; axis.orient = function(x) { if (!arguments.length) return orient; orient = x in d3_svg_axisOrients ? x + "" : d3_svg_axisDefaultOrient; return axis; }; axis.ticks = function() { if (!arguments.length) return tickArguments_; tickArguments_ = d3_array(arguments); return axis; }; axis.tickValues = function(x) { if (!arguments.length) return tickValues; tickValues = x; return axis; }; axis.tickFormat = function(x) { if (!arguments.length) return tickFormat_; tickFormat_ = x; return axis; }; axis.tickSize = function(x) { var n = arguments.length; if (!n) return innerTickSize; innerTickSize = +x; outerTickSize = +arguments[n - 1]; return axis; }; axis.innerTickSize = function(x) { if (!arguments.length) return innerTickSize; innerTickSize = +x; return axis; }; axis.outerTickSize = function(x) { if (!arguments.length) return outerTickSize; outerTickSize = +x; return axis; }; axis.tickPadding = function(x) { if (!arguments.length) return tickPadding; tickPadding = +x; return axis; }; axis.tickSubdivide = function() { return arguments.length && axis; }; return axis; }; var d3_svg_axisDefaultOrient = "bottom", d3_svg_axisOrients = { top: 1, right: 1, bottom: 1, left: 1 }; function d3_svg_axisX(selection, x0, x1) { selection.attr("transform", function(d) { var v0 = x0(d); return "translate(" + (isFinite(v0) ? v0 : x1(d)) + ",0)"; }); } function d3_svg_axisY(selection, y0, y1) { selection.attr("transform", function(d) { var v0 = y0(d); return "translate(0," + (isFinite(v0) ? v0 : y1(d)) + ")"; }); } d3.svg.brush = function() { var event = d3_eventDispatch(brush, "brushstart", "brush", "brushend"), x = null, y = null, xExtent = [ 0, 0 ], yExtent = [ 0, 0 ], xExtentDomain, yExtentDomain, xClamp = true, yClamp = true, resizes = d3_svg_brushResizes[0]; function brush(g) { g.each(function() { var g = d3.select(this).style("pointer-events", "all").style("-webkit-tap-highlight-color", "rgba(0,0,0,0)").on("mousedown.brush", brushstart).on("touchstart.brush", brushstart); var background = g.selectAll(".background").data([ 0 ]); background.enter().append("rect").attr("class", "background").style("visibility", "hidden").style("cursor", "crosshair"); g.selectAll(".extent").data([ 0 ]).enter().append("rect").attr("class", "extent").style("cursor", "move"); var resize = g.selectAll(".resize").data(resizes, d3_identity); resize.exit().remove(); resize.enter().append("g").attr("class", function(d) { return "resize " + d; }).style("cursor", function(d) { return d3_svg_brushCursor[d]; }).append("rect").attr("x", function(d) { return /[ew]$/.test(d) ? -3 : null; }).attr("y", function(d) { return /^[ns]/.test(d) ? -3 : null; }).attr("width", 6).attr("height", 6).style("visibility", "hidden"); resize.style("display", brush.empty() ? "none" : null); var gUpdate = d3.transition(g), backgroundUpdate = d3.transition(background), range; if (x) { range = d3_scaleRange(x); backgroundUpdate.attr("x", range[0]).attr("width", range[1] - range[0]); redrawX(gUpdate); } if (y) { range = d3_scaleRange(y); backgroundUpdate.attr("y", range[0]).attr("height", range[1] - range[0]); redrawY(gUpdate); } redraw(gUpdate); }); } brush.event = function(g) { g.each(function() { var event_ = event.of(this, arguments), extent1 = { x: xExtent, y: yExtent, i: xExtentDomain, j: yExtentDomain }, extent0 = this.__chart__ || extent1; this.__chart__ = extent1; if (d3_transitionInheritId) { d3.select(this).transition().each("start.brush", function() { xExtentDomain = extent0.i; yExtentDomain = extent0.j; xExtent = extent0.x; yExtent = extent0.y; event_({ type: "brushstart" }); }).tween("brush:brush", function() { var xi = d3_interpolateArray(xExtent, extent1.x), yi = d3_interpolateArray(yExtent, extent1.y); xExtentDomain = yExtentDomain = null; return function(t) { xExtent = extent1.x = xi(t); yExtent = extent1.y = yi(t); event_({ type: "brush", mode: "resize" }); }; }).each("end.brush", function() { xExtentDomain = extent1.i; yExtentDomain = extent1.j; event_({ type: "brush", mode: "resize" }); event_({ type: "brushend" }); }); } else { event_({ type: "brushstart" }); event_({ type: "brush", mode: "resize" }); event_({ type: "brushend" }); } }); }; function redraw(g) { g.selectAll(".resize").attr("transform", function(d) { return "translate(" + xExtent[+/e$/.test(d)] + "," + yExtent[+/^s/.test(d)] + ")"; }); } function redrawX(g) { g.select(".extent").attr("x", xExtent[0]); g.selectAll(".extent,.n>rect,.s>rect").attr("width", xExtent[1] - xExtent[0]); } function redrawY(g) { g.select(".extent").attr("y", yExtent[0]); g.selectAll(".extent,.e>rect,.w>rect").attr("height", yExtent[1] - yExtent[0]); } function brushstart() { var target = this, eventTarget = d3.select(d3.event.target), event_ = event.of(target, arguments), g = d3.select(target), resizing = eventTarget.datum(), resizingX = !/^(n|s)$/.test(resizing) && x, resizingY = !/^(e|w)$/.test(resizing) && y, dragging = eventTarget.classed("extent"), dragRestore = d3_event_dragSuppress(target), center, origin = d3.mouse(target), offset; var w = d3.select(d3_window(target)).on("keydown.brush", keydown).on("keyup.brush", keyup); if (d3.event.changedTouches) { w.on("touchmove.brush", brushmove).on("touchend.brush", brushend); } else { w.on("mousemove.brush", brushmove).on("mouseup.brush", brushend); } g.interrupt().selectAll("*").interrupt(); if (dragging) { origin[0] = xExtent[0] - origin[0]; origin[1] = yExtent[0] - origin[1]; } else if (resizing) { var ex = +/w$/.test(resizing), ey = +/^n/.test(resizing); offset = [ xExtent[1 - ex] - origin[0], yExtent[1 - ey] - origin[1] ]; origin[0] = xExtent[ex]; origin[1] = yExtent[ey]; } else if (d3.event.altKey) center = origin.slice(); g.style("pointer-events", "none").selectAll(".resize").style("display", null); d3.select("body").style("cursor", eventTarget.style("cursor")); event_({ type: "brushstart" }); brushmove(); function keydown() { if (d3.event.keyCode == 32) { if (!dragging) { center = null; origin[0] -= xExtent[1]; origin[1] -= yExtent[1]; dragging = 2; } d3_eventPreventDefault(); } } function keyup() { if (d3.event.keyCode == 32 && dragging == 2) { origin[0] += xExtent[1]; origin[1] += yExtent[1]; dragging = 0; d3_eventPreventDefault(); } } function brushmove() { var point = d3.mouse(target), moved = false; if (offset) { point[0] += offset[0]; point[1] += offset[1]; } if (!dragging) { if (d3.event.altKey) { if (!center) center = [ (xExtent[0] + xExtent[1]) / 2, (yExtent[0] + yExtent[1]) / 2 ]; origin[0] = xExtent[+(point[0] < center[0])]; origin[1] = yExtent[+(point[1] < center[1])]; } else center = null; } if (resizingX && move1(point, x, 0)) { redrawX(g); moved = true; } if (resizingY && move1(point, y, 1)) { redrawY(g); moved = true; } if (moved) { redraw(g); event_({ type: "brush", mode: dragging ? "move" : "resize" }); } } function move1(point, scale, i) { var range = d3_scaleRange(scale), r0 = range[0], r1 = range[1], position = origin[i], extent = i ? yExtent : xExtent, size = extent[1] - extent[0], min, max; if (dragging) { r0 -= position; r1 -= size + position; } min = (i ? yClamp : xClamp) ? Math.max(r0, Math.min(r1, point[i])) : point[i]; if (dragging) { max = (min += position) + size; } else { if (center) position = Math.max(r0, Math.min(r1, 2 * center[i] - min)); if (position < min) { max = min; min = position; } else { max = position; } } if (extent[0] != min || extent[1] != max) { if (i) yExtentDomain = null; else xExtentDomain = null; extent[0] = min; extent[1] = max; return true; } } function brushend() { brushmove(); g.style("pointer-events", "all").selectAll(".resize").style("display", brush.empty() ? "none" : null); d3.select("body").style("cursor", null); w.on("mousemove.brush", null).on("mouseup.brush", null).on("touchmove.brush", null).on("touchend.brush", null).on("keydown.brush", null).on("keyup.brush", null); dragRestore(); event_({ type: "brushend" }); } } brush.x = function(z) { if (!arguments.length) return x; x = z; resizes = d3_svg_brushResizes[!x << 1 | !y]; return brush; }; brush.y = function(z) { if (!arguments.length) return y; y = z; resizes = d3_svg_brushResizes[!x << 1 | !y]; return brush; }; brush.clamp = function(z) { if (!arguments.length) return x && y ? [ xClamp, yClamp ] : x ? xClamp : y ? yClamp : null; if (x && y) xClamp = !!z[0], yClamp = !!z[1]; else if (x) xClamp = !!z; else if (y) yClamp = !!z; return brush; }; brush.extent = function(z) { var x0, x1, y0, y1, t; if (!arguments.length) { if (x) { if (xExtentDomain) { x0 = xExtentDomain[0], x1 = xExtentDomain[1]; } else { x0 = xExtent[0], x1 = xExtent[1]; if (x.invert) x0 = x.invert(x0), x1 = x.invert(x1); if (x1 < x0) t = x0, x0 = x1, x1 = t; } } if (y) { if (yExtentDomain) { y0 = yExtentDomain[0], y1 = yExtentDomain[1]; } else { y0 = yExtent[0], y1 = yExtent[1]; if (y.invert) y0 = y.invert(y0), y1 = y.invert(y1); if (y1 < y0) t = y0, y0 = y1, y1 = t; } } return x && y ? [ [ x0, y0 ], [ x1, y1 ] ] : x ? [ x0, x1 ] : y && [ y0, y1 ]; } if (x) { x0 = z[0], x1 = z[1]; if (y) x0 = x0[0], x1 = x1[0]; xExtentDomain = [ x0, x1 ]; if (x.invert) x0 = x(x0), x1 = x(x1); if (x1 < x0) t = x0, x0 = x1, x1 = t; if (x0 != xExtent[0] || x1 != xExtent[1]) xExtent = [ x0, x1 ]; } if (y) { y0 = z[0], y1 = z[1]; if (x) y0 = y0[1], y1 = y1[1]; yExtentDomain = [ y0, y1 ]; if (y.invert) y0 = y(y0), y1 = y(y1); if (y1 < y0) t = y0, y0 = y1, y1 = t; if (y0 != yExtent[0] || y1 != yExtent[1]) yExtent = [ y0, y1 ]; } return brush; }; brush.clear = function() { if (!brush.empty()) { xExtent = [ 0, 0 ], yExtent = [ 0, 0 ]; xExtentDomain = yExtentDomain = null; } return brush; }; brush.empty = function() { return !!x && xExtent[0] == xExtent[1] || !!y && yExtent[0] == yExtent[1]; }; return d3.rebind(brush, event, "on"); }; var d3_svg_brushCursor = { n: "ns-resize", e: "ew-resize", s: "ns-resize", w: "ew-resize", nw: "nwse-resize", ne: "nesw-resize", se: "nwse-resize", sw: "nesw-resize" }; var d3_svg_brushResizes = [ [ "n", "e", "s", "w", "nw", "ne", "se", "sw" ], [ "e", "w" ], [ "n", "s" ], [] ]; var d3_time_format = d3_time.format = d3_locale_enUS.timeFormat; var d3_time_formatUtc = d3_time_format.utc; var d3_time_formatIso = d3_time_formatUtc("%Y-%m-%dT%H:%M:%S.%LZ"); d3_time_format.iso = Date.prototype.toISOString && +new Date("2000-01-01T00:00:00.000Z") ? d3_time_formatIsoNative : d3_time_formatIso; function d3_time_formatIsoNative(date) { return date.toISOString(); } d3_time_formatIsoNative.parse = function(string) { var date = new Date(string); return isNaN(date) ? null : date; }; d3_time_formatIsoNative.toString = d3_time_formatIso.toString; d3_time.second = d3_time_interval(function(date) { return new d3_date(Math.floor(date / 1e3) * 1e3); }, function(date, offset) { date.setTime(date.getTime() + Math.floor(offset) * 1e3); }, function(date) { return date.getSeconds(); }); d3_time.seconds = d3_time.second.range; d3_time.seconds.utc = d3_time.second.utc.range; d3_time.minute = d3_time_interval(function(date) { return new d3_date(Math.floor(date / 6e4) * 6e4); }, function(date, offset) { date.setTime(date.getTime() + Math.floor(offset) * 6e4); }, function(date) { return date.getMinutes(); }); d3_time.minutes = d3_time.minute.range; d3_time.minutes.utc = d3_time.minute.utc.range; d3_time.hour = d3_time_interval(function(date) { var timezone = date.getTimezoneOffset() / 60; return new d3_date((Math.floor(date / 36e5 - timezone) + timezone) * 36e5); }, function(date, offset) { date.setTime(date.getTime() + Math.floor(offset) * 36e5); }, function(date) { return date.getHours(); }); d3_time.hours = d3_time.hour.range; d3_time.hours.utc = d3_time.hour.utc.range; d3_time.month = d3_time_interval(function(date) { date = d3_time.day(date); date.setDate(1); return date; }, function(date, offset) { date.setMonth(date.getMonth() + offset); }, function(date) { return date.getMonth(); }); d3_time.months = d3_time.month.range; d3_time.months.utc = d3_time.month.utc.range; function d3_time_scale(linear, methods, format) { function scale(x) { return linear(x); } scale.invert = function(x) { return d3_time_scaleDate(linear.invert(x)); }; scale.domain = function(x) { if (!arguments.length) return linear.domain().map(d3_time_scaleDate); linear.domain(x); return scale; }; function tickMethod(extent, count) { var span = extent[1] - extent[0], target = span / count, i = d3.bisect(d3_time_scaleSteps, target); return i == d3_time_scaleSteps.length ? [ methods.year, d3_scale_linearTickRange(extent.map(function(d) { return d / 31536e6; }), count)[2] ] : !i ? [ d3_time_scaleMilliseconds, d3_scale_linearTickRange(extent, count)[2] ] : methods[target / d3_time_scaleSteps[i - 1] < d3_time_scaleSteps[i] / target ? i - 1 : i]; } scale.nice = function(interval, skip) { var domain = scale.domain(), extent = d3_scaleExtent(domain), method = interval == null ? tickMethod(extent, 10) : typeof interval === "number" && tickMethod(extent, interval); if (method) interval = method[0], skip = method[1]; function skipped(date) { return !isNaN(date) && !interval.range(date, d3_time_scaleDate(+date + 1), skip).length; } return scale.domain(d3_scale_nice(domain, skip > 1 ? { floor: function(date) { while (skipped(date = interval.floor(date))) date = d3_time_scaleDate(date - 1); return date; }, ceil: function(date) { while (skipped(date = interval.ceil(date))) date = d3_time_scaleDate(+date + 1); return date; } } : interval)); }; scale.ticks = function(interval, skip) { var extent = d3_scaleExtent(scale.domain()), method = interval == null ? tickMethod(extent, 10) : typeof interval === "number" ? tickMethod(extent, interval) : !interval.range && [ { range: interval }, skip ]; if (method) interval = method[0], skip = method[1]; return interval.range(extent[0], d3_time_scaleDate(+extent[1] + 1), skip < 1 ? 1 : skip); }; scale.tickFormat = function() { return format; }; scale.copy = function() { return d3_time_scale(linear.copy(), methods, format); }; return d3_scale_linearRebind(scale, linear); } function d3_time_scaleDate(t) { return new Date(t); } var d3_time_scaleSteps = [ 1e3, 5e3, 15e3, 3e4, 6e4, 3e5, 9e5, 18e5, 36e5, 108e5, 216e5, 432e5, 864e5, 1728e5, 6048e5, 2592e6, 7776e6, 31536e6 ]; var d3_time_scaleLocalMethods = [ [ d3_time.second, 1 ], [ d3_time.second, 5 ], [ d3_time.second, 15 ], [ d3_time.second, 30 ], [ d3_time.minute, 1 ], [ d3_time.minute, 5 ], [ d3_time.minute, 15 ], [ d3_time.minute, 30 ], [ d3_time.hour, 1 ], [ d3_time.hour, 3 ], [ d3_time.hour, 6 ], [ d3_time.hour, 12 ], [ d3_time.day, 1 ], [ d3_time.day, 2 ], [ d3_time.week, 1 ], [ d3_time.month, 1 ], [ d3_time.month, 3 ], [ d3_time.year, 1 ] ]; var d3_time_scaleLocalFormat = d3_time_format.multi([ [ ".%L", function(d) { return d.getMilliseconds(); } ], [ ":%S", function(d) { return d.getSeconds(); } ], [ "%I:%M", function(d) { return d.getMinutes(); } ], [ "%I %p", function(d) { return d.getHours(); } ], [ "%a %d", function(d) { return d.getDay() && d.getDate() != 1; } ], [ "%b %d", function(d) { return d.getDate() != 1; } ], [ "%B", function(d) { return d.getMonth(); } ], [ "%Y", d3_true ] ]); var d3_time_scaleMilliseconds = { range: function(start, stop, step) { return d3.range(Math.ceil(start / step) * step, +stop, step).map(d3_time_scaleDate); }, floor: d3_identity, ceil: d3_identity }; d3_time_scaleLocalMethods.year = d3_time.year; d3_time.scale = function() { return d3_time_scale(d3.scale.linear(), d3_time_scaleLocalMethods, d3_time_scaleLocalFormat); }; var d3_time_scaleUtcMethods = d3_time_scaleLocalMethods.map(function(m) { return [ m[0].utc, m[1] ]; }); var d3_time_scaleUtcFormat = d3_time_formatUtc.multi([ [ ".%L", function(d) { return d.getUTCMilliseconds(); } ], [ ":%S", function(d) { return d.getUTCSeconds(); } ], [ "%I:%M", function(d) { return d.getUTCMinutes(); } ], [ "%I %p", function(d) { return d.getUTCHours(); } ], [ "%a %d", function(d) { return d.getUTCDay() && d.getUTCDate() != 1; } ], [ "%b %d", function(d) { return d.getUTCDate() != 1; } ], [ "%B", function(d) { return d.getUTCMonth(); } ], [ "%Y", d3_true ] ]); d3_time_scaleUtcMethods.year = d3_time.year.utc; d3_time.scale.utc = function() { return d3_time_scale(d3.scale.linear(), d3_time_scaleUtcMethods, d3_time_scaleUtcFormat); }; d3.text = d3_xhrType(function(request) { return request.responseText; }); d3.json = function(url, callback) { return d3_xhr(url, "application/json", d3_json, callback); }; function d3_json(request) { return JSON.parse(request.responseText); } d3.html = function(url, callback) { return d3_xhr(url, "text/html", d3_html, callback); }; function d3_html(request) { var range = d3_document.createRange(); range.selectNode(d3_document.body); return range.createContextualFragment(request.responseText); } d3.xml = d3_xhrType(function(request) { return request.responseXML; }); if (true) this.d3 = d3, !(__WEBPACK_AMD_DEFINE_FACTORY__ = (d3), __WEBPACK_AMD_DEFINE_RESULT__ = (typeof __WEBPACK_AMD_DEFINE_FACTORY__ === 'function' ? (__WEBPACK_AMD_DEFINE_FACTORY__.call(exports, __webpack_require__, exports, module)) : __WEBPACK_AMD_DEFINE_FACTORY__), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__)); else if (typeof module === "object" && module.exports) module.exports = d3; else this.d3 = d3; }(); /***/ }), /* 3 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; Object.defineProperty(exports, "__esModule", { value: true }); var _d = __webpack_require__(2); var _d2 = _interopRequireDefault(_d); function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } var Barchart = // svg: d3 object with the svg in question // exp_array: list of (feature_name, weight) function Barchart(svg, exp_array) { var two_sided = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : true; var titles = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : undefined; var colors = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : ['red', 'green']; var show_numbers = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : false; var bar_height = arguments.length > 6 && arguments[6] !== undefined ? arguments[6] : 5; _classCallCheck(this, Barchart); var svg_width = Math.min(600, parseInt(svg.style('width'))); var bar_width = two_sided ? svg_width / 2 : svg_width; if (titles === undefined) { titles = two_sided ? ['Cons', 'Pros'] : 'Pros'; } if (show_numbers) { bar_width = bar_width - 30; } var x_offset = two_sided ? svg_width / 2 : 10; // 13.1 is +- the width of W, the widest letter. if (two_sided && titles.length == 2) { svg.append('text').attr('x', svg_width / 4).attr('y', 15).attr('font-size', '20').attr('text-anchor', 'middle').style('fill', colors[0]).text(titles[0]); svg.append('text').attr('x', svg_width / 4 * 3).attr('y', 15).attr('font-size', '20').attr('text-anchor', 'middle').style('fill', colors[1]).text(titles[1]); } else { var pos = two_sided ? svg_width / 2 : x_offset; var anchor = two_sided ? 'middle' : 'begin'; svg.append('text').attr('x', pos).attr('y', 15).attr('font-size', '20').attr('text-anchor', anchor).text(titles); } var yshift = 20; var space_between_bars = 0; var text_height = 16; var space_between_bar_and_text = 3; var total_bar_height = text_height + space_between_bar_and_text + bar_height + space_between_bars; var total_height = total_bar_height * exp_array.length; this.svg_height = total_height + yshift; var yscale = _d2.default.scale.linear().domain([0, exp_array.length]).range([yshift, yshift + total_height]); var names = exp_array.map(function (v) { return v[0]; }); var weights = exp_array.map(function (v) { return v[1]; }); var max_weight = Math.max.apply(Math, _toConsumableArray(weights.map(function (v) { return Math.abs(v); }))); var xscale = _d2.default.scale.linear().domain([0, Math.max(1, max_weight)]).range([0, bar_width]); for (var i = 0; i < exp_array.length; ++i) { var name = names[i]; var weight = weights[i]; var size = xscale(Math.abs(weight)); var to_the_right = weight > 0 || !two_sided; var text = svg.append('text').attr('x', to_the_right ? x_offset + 2 : x_offset - 2).attr('y', yscale(i) + text_height).attr('text-anchor', to_the_right ? 'begin' : 'end').attr('font-size', '14').text(name); while (text.node().getBBox()['width'] + 1 > bar_width) { var cur_text = text.text().slice(0, text.text().length - 5); text.text(cur_text + '...'); if (text === '...') { break; } } var bar = svg.append('rect').attr('height', bar_height).attr('x', to_the_right ? x_offset : x_offset - size).attr('y', text_height + yscale(i) + space_between_bar_and_text) // + bar_height) .attr('width', size).style('fill', weight > 0 ? colors[1] : colors[0]); if (show_numbers) { var bartext = svg.append('text').attr('x', to_the_right ? x_offset + size + 1 : x_offset - size - 1).attr('text-anchor', weight > 0 || !two_sided ? 'begin' : 'end').attr('y', bar_height + yscale(i) + text_height + space_between_bar_and_text).attr('font-size', '10').text(Math.abs(weight).toFixed(2)); } } var line = svg.append("line").attr("x1", x_offset).attr("x2", x_offset).attr("y1", bar_height + yshift).attr("y2", Math.max(bar_height, yscale(exp_array.length))).style("stroke-width", 2).style("stroke", "black"); }; exports.default = Barchart; /***/ }), /* 4 */ /***/ (function(module, exports, __webpack_require__) { var __WEBPACK_AMD_DEFINE_RESULT__;/* WEBPACK VAR INJECTION */(function(global, module) {/** * @license * Lodash <https://lodash.com/> * Copyright JS Foundation and other contributors <https://js.foundation/> * Released under MIT license <https://lodash.com/license> * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE> * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors */ ;(function() { /** Used as a safe reference for `undefined` in pre-ES5 environments. */ var undefined; /** Used as the semantic version number. */ var VERSION = '4.17.4'; /** Used as the size to enable large array optimizations. */ var LARGE_ARRAY_SIZE = 200; /** Error message constants. */ var CORE_ERROR_TEXT = 'Unsupported core-js use. Try https://npms.io/search?q=ponyfill.', FUNC_ERROR_TEXT = 'Expected a function'; /** Used to stand-in for `undefined` hash values. */ var HASH_UNDEFINED = '__lodash_hash_undefined__'; /** Used as the maximum memoize cache size. */ var MAX_MEMOIZE_SIZE = 500; /** Used as the internal argument placeholder. */ var PLACEHOLDER = '__lodash_placeholder__'; /** Used to compose bitmasks for cloning. */ var CLONE_DEEP_FLAG = 1, CLONE_FLAT_FLAG = 2, CLONE_SYMBOLS_FLAG = 4; /** Used to compose bitmasks for value comparisons. */ var COMPARE_PARTIAL_FLAG = 1, COMPARE_UNORDERED_FLAG = 2; /** Used to compose bitmasks for function metadata. */ var WRAP_BIND_FLAG = 1, WRAP_BIND_KEY_FLAG = 2, WRAP_CURRY_BOUND_FLAG = 4, WRAP_CURRY_FLAG = 8, WRAP_CURRY_RIGHT_FLAG = 16, WRAP_PARTIAL_FLAG = 32, WRAP_PARTIAL_RIGHT_FLAG = 64, WRAP_ARY_FLAG = 128, WRAP_REARG_FLAG = 256, WRAP_FLIP_FLAG = 512; /** Used as default options for `_.truncate`. */ var DEFAULT_TRUNC_LENGTH = 30, DEFAULT_TRUNC_OMISSION = '...'; /** Used to detect hot functions by number of calls within a span of milliseconds. */ var HOT_COUNT = 800, HOT_SPAN = 16; /** Used to indicate the type of lazy iteratees. */ var LAZY_FILTER_FLAG = 1, LAZY_MAP_FLAG = 2, LAZY_WHILE_FLAG = 3; /** Used as references for various `Number` constants. */ var INFINITY = 1 / 0, MAX_SAFE_INTEGER = 9007199254740991, MAX_INTEGER = 1.7976931348623157e+308, NAN = 0 / 0; /** Used as references for the maximum length and index of an array. */ var MAX_ARRAY_LENGTH = 4294967295, MAX_ARRAY_INDEX = MAX_ARRAY_LENGTH - 1, HALF_MAX_ARRAY_LENGTH = MAX_ARRAY_LENGTH >>> 1; /** Used to associate wrap methods with their bit flags. */ var wrapFlags = [ ['ary', WRAP_ARY_FLAG], ['bind', WRAP_BIND_FLAG], ['bindKey', WRAP_BIND_KEY_FLAG], ['curry', WRAP_CURRY_FLAG], ['curryRight', WRAP_CURRY_RIGHT_FLAG], ['flip', WRAP_FLIP_FLAG], ['partial', WRAP_PARTIAL_FLAG], ['partialRight', WRAP_PARTIAL_RIGHT_FLAG], ['rearg', WRAP_REARG_FLAG] ]; /** `Object#toString` result references. */ var argsTag = '[object Arguments]', arrayTag = '[object Array]', asyncTag = '[object AsyncFunction]', boolTag = '[object Boolean]', dateTag = '[object Date]', domExcTag = '[object DOMException]', errorTag = '[object Error]', funcTag = '[object Function]', genTag = '[object GeneratorFunction]', mapTag = '[object Map]', numberTag = '[object Number]', nullTag = '[object Null]', objectTag = '[object Object]', promiseTag = '[object Promise]', proxyTag = '[object Proxy]', regexpTag = '[object RegExp]', setTag = '[object Set]', stringTag = '[object String]', symbolTag = '[object Symbol]', undefinedTag = '[object Undefined]', weakMapTag = '[object WeakMap]', weakSetTag = '[object WeakSet]'; var arrayBufferTag = '[object ArrayBuffer]', dataViewTag = '[object DataView]', float32Tag = '[object Float32Array]', float64Tag = '[object Float64Array]', int8Tag = '[object Int8Array]', int16Tag = '[object Int16Array]', int32Tag = '[object Int32Array]', uint8Tag = '[object Uint8Array]', uint8ClampedTag = '[object Uint8ClampedArray]', uint16Tag = '[object Uint16Array]', uint32Tag = '[object Uint32Array]'; /** Used to match empty string literals in compiled template source. */ var reEmptyStringLeading = /\b__p \+= '';/g, reEmptyStringMiddle = /\b(__p \+=) '' \+/g, reEmptyStringTrailing = /(__e\(.*?\)|\b__t\)) \+\n'';/g; /** Used to match HTML entities and HTML characters. */ var reEscapedHtml = /&(?:amp|lt|gt|quot|#39);/g, reUnescapedHtml = /[&<>"']/g, reHasEscapedHtml = RegExp(reEscapedHtml.source), reHasUnescapedHtml = RegExp(reUnescapedHtml.source); /** Used to match template delimiters. */ var reEscape = /<%-([\s\S]+?)%>/g, reEvaluate = /<%([\s\S]+?)%>/g, reInterpolate = /<%=([\s\S]+?)%>/g; /** Used to match property names within property paths. */ var reIsDeepProp = /\.|\[(?:[^[\]]*|(["'])(?:(?!\1)[^\\]|\\.)*?\1)\]/, reIsPlainProp = /^\w*$/, reLeadingDot = /^\./, rePropName = /[^.[\]]+|\[(?:(-?\d+(?:\.\d+)?)|(["'])((?:(?!\2)[^\\]|\\.)*?)\2)\]|(?=(?:\.|\[\])(?:\.|\[\]|$))/g; /** * Used to match `RegExp` * [syntax characters](http://ecma-international.org/ecma-262/7.0/#sec-patterns). */ var reRegExpChar = /[\\^$.*+?()[\]{}|]/g, reHasRegExpChar = RegExp(reRegExpChar.source); /** Used to match leading and trailing whitespace. */ var reTrim = /^\s+|\s+$/g, reTrimStart = /^\s+/, reTrimEnd = /\s+$/; /** Used to match wrap detail comments. */ var reWrapComment = /\{(?:\n\/\* \[wrapped with .+\] \*\/)?\n?/, reWrapDetails = /\{\n\/\* \[wrapped with (.+)\] \*/, reSplitDetails = /,? & /; /** Used to match words composed of alphanumeric characters. */ var reAsciiWord = /[^\x00-\x2f\x3a-\x40\x5b-\x60\x7b-\x7f]+/g; /** Used to match backslashes in property paths. */ var reEscapeChar = /\\(\\)?/g; /** * Used to match * [ES template delimiters](http://ecma-international.org/ecma-262/7.0/#sec-template-literal-lexical-components). */ var reEsTemplate = /\$\{([^\\}]*(?:\\.[^\\}]*)*)\}/g; /** Used to match `RegExp` flags from their coerced string values. */ var reFlags = /\w*$/; /** Used to detect bad signed hexadecimal string values. */ var reIsBadHex = /^[-+]0x[0-9a-f]+$/i; /** Used to detect binary string values. */ var reIsBinary = /^0b[01]+$/i; /** Used to detect host constructors (Safari). */ var reIsHostCtor = /^\[object .+?Constructor\]$/; /** Used to detect octal string values. */ var reIsOctal = /^0o[0-7]+$/i; /** Used to detect unsigned integer values. */ var reIsUint = /^(?:0|[1-9]\d*)$/; /** Used to match Latin Unicode letters (excluding mathematical operators). */ var reLatin = /[\xc0-\xd6\xd8-\xf6\xf8-\xff\u0100-\u017f]/g; /** Used to ensure capturing order of template delimiters. */ var reNoMatch = /($^)/; /** Used to match unescaped characters in compiled string literals. */ var reUnescapedString = /['\n\r\u2028\u2029\\]/g; /** Used to compose unicode character classes. */ var rsAstralRange = '\\ud800-\\udfff', rsComboMarksRange = '\\u0300-\\u036f', reComboHalfMarksRange = '\\ufe20-\\ufe2f', rsComboSymbolsRange = '\\u20d0-\\u20ff', rsComboRange = rsComboMarksRange + reComboHalfMarksRange + rsComboSymbolsRange, rsDingbatRange = '\\u2700-\\u27bf', rsLowerRange = 'a-z\\xdf-\\xf6\\xf8-\\xff', rsMathOpRange = '\\xac\\xb1\\xd7\\xf7', rsNonCharRange = '\\x00-\\x2f\\x3a-\\x40\\x5b-\\x60\\x7b-\\xbf', rsPunctuationRange = '\\u2000-\\u206f', rsSpaceRange = ' \\t\\x0b\\f\\xa0\\ufeff\\n\\r\\u2028\\u2029\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000', rsUpperRange = 'A-Z\\xc0-\\xd6\\xd8-\\xde', rsVarRange = '\\ufe0e\\ufe0f', rsBreakRange = rsMathOpRange + rsNonCharRange + rsPunctuationRange + rsSpaceRange; /** Used to compose unicode capture groups. */ var rsApos = "['\u2019]", rsAstral = '[' + rsAstralRange + ']', rsBreak = '[' + rsBreakRange + ']', rsCombo = '[' + rsComboRange + ']', rsDigits = '\\d+', rsDingbat = '[' + rsDingbatRange + ']', rsLower = '[' + rsLowerRange + ']', rsMisc = '[^' + rsAstralRange + rsBreakRange + rsDigits + rsDingbatRange + rsLowerRange + rsUpperRange + ']', rsFitz = '\\ud83c[\\udffb-\\udfff]', rsModifier = '(?:' + rsCombo + '|' + rsFitz + ')', rsNonAstral = '[^' + rsAstralRange + ']', rsRegional = '(?:\\ud83c[\\udde6-\\uddff]){2}', rsSurrPair = '[\\ud800-\\udbff][\\udc00-\\udfff]', rsUpper = '[' + rsUpperRange + ']', rsZWJ = '\\u200d'; /** Used to compose unicode regexes. */ var rsMiscLower = '(?:' + rsLower + '|' + rsMisc + ')', rsMiscUpper = '(?:' + rsUpper + '|' + rsMisc + ')', rsOptContrLower = '(?:' + rsApos + '(?:d|ll|m|re|s|t|ve))?', rsOptContrUpper = '(?:' + rsApos + '(?:D|LL|M|RE|S|T|VE))?', reOptMod = rsModifier + '?', rsOptVar = '[' + rsVarRange + ']?', rsOptJoin = '(?:' + rsZWJ + '(?:' + [rsNonAstral, rsRegional, rsSurrPair].join('|') + ')' + rsOptVar + reOptMod + ')*', rsOrdLower = '\\d*(?:(?:1st|2nd|3rd|(?![123])\\dth)\\b)', rsOrdUpper = '\\d*(?:(?:1ST|2ND|3RD|(?![123])\\dTH)\\b)', rsSeq = rsOptVar + reOptMod + rsOptJoin, rsEmoji = '(?:' + [rsDingbat, rsRegional, rsSurrPair].join('|') + ')' + rsSeq, rsSymbol = '(?:' + [rsNonAstral + rsCombo + '?', rsCombo, rsRegional, rsSurrPair, rsAstral].join('|') + ')'; /** Used to match apostrophes. */ var reApos = RegExp(rsApos, 'g'); /** * Used to match [combining diacritical marks](https://en.wikipedia.org/wiki/Combining_Diacritical_Marks) and * [combining diacritical marks for symbols](https://en.wikipedia.org/wiki/Combining_Diacritical_Marks_for_Symbols). */ var reComboMark = RegExp(rsCombo, 'g'); /** Used to match [string symbols](https://mathiasbynens.be/notes/javascript-unicode). */ var reUnicode = RegExp(rsFitz + '(?=' + rsFitz + ')|' + rsSymbol + rsSeq, 'g'); /** Used to match complex or compound words. */ var reUnicodeWord = RegExp([ rsUpper + '?' + rsLower + '+' + rsOptContrLower + '(?=' + [rsBreak, rsUpper, '$'].join('|') + ')', rsMiscUpper + '+' + rsOptContrUpper + '(?=' + [rsBreak, rsUpper + rsMiscLower, '$'].join('|') + ')', rsUpper + '?' + rsMiscLower + '+' + rsOptContrLower, rsUpper + '+' + rsOptContrUpper, rsOrdUpper, rsOrdLower, rsDigits, rsEmoji ].join('|'), 'g'); /** Used to detect strings with [zero-width joiners or code points from the astral planes](http://eev.ee/blog/2015/09/12/dark-corners-of-unicode/). */ var reHasUnicode = RegExp('[' + rsZWJ + rsAstralRange + rsComboRange + rsVarRange + ']'); /** Used to detect strings that need a more robust regexp to match words. */ var reHasUnicodeWord = /[a-z][A-Z]|[A-Z]{2,}[a-z]|[0-9][a-zA-Z]|[a-zA-Z][0-9]|[^a-zA-Z0-9 ]/; /** Used to assign default `context` object properties. */ var contextProps = [ 'Array', 'Buffer', 'DataView', 'Date', 'Error', 'Float32Array', 'Float64Array', 'Function', 'Int8Array', 'Int16Array', 'Int32Array', 'Map', 'Math', 'Object', 'Promise', 'RegExp', 'Set', 'String', 'Symbol', 'TypeError', 'Uint8Array', 'Uint8ClampedArray', 'Uint16Array', 'Uint32Array', 'WeakMap', '_', 'clearTimeout', 'isFinite', 'parseInt', 'setTimeout' ]; /** Used to make template sourceURLs easier to identify. */ var templateCounter = -1; /** Used to identify `toStringTag` values of typed arrays. */ var typedArrayTags = {}; typedArrayTags[float32Tag] = typedArrayTags[float64Tag] = typedArrayTags[int8Tag] = typedArrayTags[int16Tag] = typedArrayTags[int32Tag] = typedArrayTags[uint8Tag] = typedArrayTags[uint8ClampedTag] = typedArrayTags[uint16Tag] = typedArrayTags[uint32Tag] = true; typedArrayTags[argsTag] = typedArrayTags[arrayTag] = typedArrayTags[arrayBufferTag] = typedArrayTags[boolTag] = typedArrayTags[dataViewTag] = typedArrayTags[dateTag] = typedArrayTags[errorTag] = typedArrayTags[funcTag] = typedArrayTags[mapTag] = typedArrayTags[numberTag] = typedArrayTags[objectTag] = typedArrayTags[regexpTag] = typedArrayTags[setTag] = typedArrayTags[stringTag] = typedArrayTags[weakMapTag] = false; /** Used to identify `toStringTag` values supported by `_.clone`. */ var cloneableTags = {}; cloneableTags[argsTag] = cloneableTags[arrayTag] = cloneableTags[arrayBufferTag] = cloneableTags[dataViewTag] = cloneableTags[boolTag] = cloneableTags[dateTag] = cloneableTags[float32Tag] = cloneableTags[float64Tag] = cloneableTags[int8Tag] = cloneableTags[int16Tag] = cloneableTags[int32Tag] = cloneableTags[mapTag] = cloneableTags[numberTag] = cloneableTags[objectTag] = cloneableTags[regexpTag] = cloneableTags[setTag] = cloneableTags[stringTag] = cloneableTags[symbolTag] = cloneableTags[uint8Tag] = cloneableTags[uint8ClampedTag] = cloneableTags[uint16Tag] = cloneableTags[uint32Tag] = true; cloneableTags[errorTag] = cloneableTags[funcTag] = cloneableTags[weakMapTag] = false; /** Used to map Latin Unicode letters to basic Latin letters. */ var deburredLetters = { // Latin-1 Supplement block. '\xc0': 'A', '\xc1': 'A', '\xc2': 'A', '\xc3': 'A', '\xc4': 'A', '\xc5': 'A', '\xe0': 'a', '\xe1': 'a', '\xe2': 'a', '\xe3': 'a', '\xe4': 'a', '\xe5': 'a', '\xc7': 'C', '\xe7': 'c', '\xd0': 'D', '\xf0': 'd', '\xc8': 'E', '\xc9': 'E', '\xca': 'E', '\xcb': 'E', '\xe8': 'e', '\xe9': 'e', '\xea': 'e', '\xeb': 'e', '\xcc': 'I', '\xcd': 'I', '\xce': 'I', '\xcf': 'I', '\xec': 'i', '\xed': 'i', '\xee': 'i', '\xef': 'i', '\xd1': 'N', '\xf1': 'n', '\xd2': 'O', '\xd3': 'O', '\xd4': 'O', '\xd5': 'O', '\xd6': 'O', '\xd8': 'O', '\xf2': 'o', '\xf3': 'o', '\xf4': 'o', '\xf5': 'o', '\xf6': 'o', '\xf8': 'o', '\xd9': 'U', '\xda': 'U', '\xdb': 'U', '\xdc': 'U', '\xf9': 'u', '\xfa': 'u', '\xfb': 'u', '\xfc': 'u', '\xdd': 'Y', '\xfd': 'y', '\xff': 'y', '\xc6': 'Ae', '\xe6': 'ae', '\xde': 'Th', '\xfe': 'th', '\xdf': 'ss', // Latin Extended-A block. '\u0100': 'A', '\u0102': 'A', '\u0104': 'A', '\u0101': 'a', '\u0103': 'a', '\u0105': 'a', '\u0106': 'C', '\u0108': 'C', '\u010a': 'C', '\u010c': 'C', '\u0107': 'c', '\u0109': 'c', '\u010b': 'c', '\u010d': 'c', '\u010e': 'D', '\u0110': 'D', '\u010f': 'd', '\u0111': 'd', '\u0112': 'E', '\u0114': 'E', '\u0116': 'E', '\u0118': 'E', '\u011a': 'E', '\u0113': 'e', '\u0115': 'e', '\u0117': 'e', '\u0119': 'e', '\u011b': 'e', '\u011c': 'G', '\u011e': 'G', '\u0120': 'G', '\u0122': 'G', '\u011d': 'g', '\u011f': 'g', '\u0121': 'g', '\u0123': 'g', '\u0124': 'H', '\u0126': 'H', '\u0125': 'h', '\u0127': 'h', '\u0128': 'I', '\u012a': 'I', '\u012c': 'I', '\u012e': 'I', '\u0130': 'I', '\u0129': 'i', '\u012b': 'i', '\u012d': 'i', '\u012f': 'i', '\u0131': 'i', '\u0134': 'J', '\u0135': 'j', '\u0136': 'K', '\u0137': 'k', '\u0138': 'k', '\u0139': 'L', '\u013b': 'L', '\u013d': 'L', '\u013f': 'L', '\u0141': 'L', '\u013a': 'l', '\u013c': 'l', '\u013e': 'l', '\u0140': 'l', '\u0142': 'l', '\u0143': 'N', '\u0145': 'N', '\u0147': 'N', '\u014a': 'N', '\u0144': 'n', '\u0146': 'n', '\u0148': 'n', '\u014b': 'n', '\u014c': 'O', '\u014e': 'O', '\u0150': 'O', '\u014d': 'o', '\u014f': 'o', '\u0151': 'o', '\u0154': 'R', '\u0156': 'R', '\u0158': 'R', '\u0155': 'r', '\u0157': 'r', '\u0159': 'r', '\u015a': 'S', '\u015c': 'S', '\u015e': 'S', '\u0160': 'S', '\u015b': 's', '\u015d': 's', '\u015f': 's', '\u0161': 's', '\u0162': 'T', '\u0164': 'T', '\u0166': 'T', '\u0163': 't', '\u0165': 't', '\u0167': 't', '\u0168': 'U', '\u016a': 'U', '\u016c': 'U', '\u016e': 'U', '\u0170': 'U', '\u0172': 'U', '\u0169': 'u', '\u016b': 'u', '\u016d': 'u', '\u016f': 'u', '\u0171': 'u', '\u0173': 'u', '\u0174': 'W', '\u0175': 'w', '\u0176': 'Y', '\u0177': 'y', '\u0178': 'Y', '\u0179': 'Z', '\u017b': 'Z', '\u017d': 'Z', '\u017a': 'z', '\u017c': 'z', '\u017e': 'z', '\u0132': 'IJ', '\u0133': 'ij', '\u0152': 'Oe', '\u0153': 'oe', '\u0149': "'n", '\u017f': 's' }; /** Used to map characters to HTML entities. */ var htmlEscapes = { '&': '&', '<': '<', '>': '>', '"': '"', "'": ''' }; /** Used to map HTML entities to characters. */ var htmlUnescapes = { '&': '&', '<': '<', '>': '>', '"': '"', ''': "'" }; /** Used to escape characters for inclusion in compiled string literals. */ var stringEscapes = { '\\': '\\', "'": "'", '\n': 'n', '\r': 'r', '\u2028': 'u2028', '\u2029': 'u2029' }; /** Built-in method references without a dependency on `root`. */ var freeParseFloat = parseFloat, freeParseInt = parseInt; /** Detect free variable `global` from Node.js. */ var freeGlobal = typeof global == 'object' && global && global.Object === Object && global; /** Detect free variable `self`. */ var freeSelf = typeof self == 'object' && self && self.Object === Object && self; /** Used as a reference to the global object. */ var root = freeGlobal || freeSelf || Function('return this')(); /** Detect free variable `exports`. */ var freeExports = typeof exports == 'object' && exports && !exports.nodeType && exports; /** Detect free variable `module`. */ var freeModule = freeExports && typeof module == 'object' && module && !module.nodeType && module; /** Detect the popular CommonJS extension `module.exports`. */ var moduleExports = freeModule && freeModule.exports === freeExports; /** Detect free variable `process` from Node.js. */ var freeProcess = moduleExports && freeGlobal.process; /** Used to access faster Node.js helpers. */ var nodeUtil = (function() { try { return freeProcess && freeProcess.binding && freeProcess.binding('util'); } catch (e) {} }()); /* Node.js helper references. */ var nodeIsArrayBuffer = nodeUtil && nodeUtil.isArrayBuffer, nodeIsDate = nodeUtil && nodeUtil.isDate, nodeIsMap = nodeUtil && nodeUtil.isMap, nodeIsRegExp = nodeUtil && nodeUtil.isRegExp, nodeIsSet = nodeUtil && nodeUtil.isSet, nodeIsTypedArray = nodeUtil && nodeUtil.isTypedArray; /*--------------------------------------------------------------------------*/ /** * Adds the key-value `pair` to `map`. * * @private * @param {Object} map The map to modify. * @param {Array} pair The key-value pair to add. * @returns {Object} Returns `map`. */ function addMapEntry(map, pair) { // Don't return `map.set` because it's not chainable in IE 11. map.set(pair[0], pair[1]); return map; } /** * Adds `value` to `set`. * * @private * @param {Object} set The set to modify. * @param {*} value The value to add. * @returns {Object} Returns `set`. */ function addSetEntry(set, value) { // Don't return `set.add` because it's not chainable in IE 11. set.add(value); return set; } /** * A faster alternative to `Function#apply`, this function invokes `func` * with the `this` binding of `thisArg` and the arguments of `args`. * * @private * @param {Function} func The function to invoke. * @param {*} thisArg The `this` binding of `func`. * @param {Array} args The arguments to invoke `func` with. * @returns {*} Returns the result of `func`. */ function apply(func, thisArg, args) { switch (args.length) { case 0: return func.call(thisArg); case 1: return func.call(thisArg, args[0]); case 2: return func.call(thisArg, args[0], args[1]); case 3: return func.call(thisArg, args[0], args[1], args[2]); } return func.apply(thisArg, args); } /** * A specialized version of `baseAggregator` for arrays. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} setter The function to set `accumulator` values. * @param {Function} iteratee The iteratee to transform keys. * @param {Object} accumulator The initial aggregated object. * @returns {Function} Returns `accumulator`. */ function arrayAggregator(array, setter, iteratee, accumulator) { var index = -1, length = array == null ? 0 : array.length; while (++index < length) { var value = array[index]; setter(accumulator, value, iteratee(value), array); } return accumulator; } /** * A specialized version of `_.forEach` for arrays without support for * iteratee shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Array} Returns `array`. */ function arrayEach(array, iteratee) { var index = -1, length = array == null ? 0 : array.length; while (++index < length) { if (iteratee(array[index], index, array) === false) { break; } } return array; } /** * A specialized version of `_.forEachRight` for arrays without support for * iteratee shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Array} Returns `array`. */ function arrayEachRight(array, iteratee) { var length = array == null ? 0 : array.length; while (length--) { if (iteratee(array[length], length, array) === false) { break; } } return array; } /** * A specialized version of `_.every` for arrays without support for * iteratee shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} predicate The function invoked per iteration. * @returns {boolean} Returns `true` if all elements pass the predicate check, * else `false`. */ function arrayEvery(array, predicate) { var index = -1, length = array == null ? 0 : array.length; while (++index < length) { if (!predicate(array[index], index, array)) { return false; } } return true; } /** * A specialized version of `_.filter` for arrays without support for * iteratee shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} predicate The function invoked per iteration. * @returns {Array} Returns the new filtered array. */ function arrayFilter(array, predicate) { var index = -1, length = array == null ? 0 : array.length, resIndex = 0, result = []; while (++index < length) { var value = array[index]; if (predicate(value, index, array)) { result[resIndex++] = value; } } return result; } /** * A specialized version of `_.includes` for arrays without support for * specifying an index to search from. * * @private * @param {Array} [array] The array to inspect. * @param {*} target The value to search for. * @returns {boolean} Returns `true` if `target` is found, else `false`. */ function arrayIncludes(array, value) { var length = array == null ? 0 : array.length; return !!length && baseIndexOf(array, value, 0) > -1; } /** * This function is like `arrayIncludes` except that it accepts a comparator. * * @private * @param {Array} [array] The array to inspect. * @param {*} target The value to search for. * @param {Function} comparator The comparator invoked per element. * @returns {boolean} Returns `true` if `target` is found, else `false`. */ function arrayIncludesWith(array, value, comparator) { var index = -1, length = array == null ? 0 : array.length; while (++index < length) { if (comparator(value, array[index])) { return true; } } return false; } /** * A specialized version of `_.map` for arrays without support for iteratee * shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Array} Returns the new mapped array. */ function arrayMap(array, iteratee) { var index = -1, length = array == null ? 0 : array.length, result = Array(length); while (++index < length) { result[index] = iteratee(array[index], index, array); } return result; } /** * Appends the elements of `values` to `array`. * * @private * @param {Array} array The array to modify. * @param {Array} values The values to append. * @returns {Array} Returns `array`. */ function arrayPush(array, values) { var index = -1, length = values.length, offset = array.length; while (++index < length) { array[offset + index] = values[index]; } return array; } /** * A specialized version of `_.reduce` for arrays without support for * iteratee shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @param {*} [accumulator] The initial value. * @param {boolean} [initAccum] Specify using the first element of `array` as * the initial value. * @returns {*} Returns the accumulated value. */ function arrayReduce(array, iteratee, accumulator, initAccum) { var index = -1, length = array == null ? 0 : array.length; if (initAccum && length) { accumulator = array[++index]; } while (++index < length) { accumulator = iteratee(accumulator, array[index], index, array); } return accumulator; } /** * A specialized version of `_.reduceRight` for arrays without support for * iteratee shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @param {*} [accumulator] The initial value. * @param {boolean} [initAccum] Specify using the last element of `array` as * the initial value. * @returns {*} Returns the accumulated value. */ function arrayReduceRight(array, iteratee, accumulator, initAccum) { var length = array == null ? 0 : array.length; if (initAccum && length) { accumulator = array[--length]; } while (length--) { accumulator = iteratee(accumulator, array[length], length, array); } return accumulator; } /** * A specialized version of `_.some` for arrays without support for iteratee * shorthands. * * @private * @param {Array} [array] The array to iterate over. * @param {Function} predicate The function invoked per iteration. * @returns {boolean} Returns `true` if any element passes the predicate check, * else `false`. */ function arraySome(array, predicate) { var index = -1, length = array == null ? 0 : array.length; while (++index < length) { if (predicate(array[index], index, array)) { return true; } } return false; } /** * Gets the size of an ASCII `string`. * * @private * @param {string} string The string inspect. * @returns {number} Returns the string size. */ var asciiSize = baseProperty('length'); /** * Converts an ASCII `string` to an array. * * @private * @param {string} string The string to convert. * @returns {Array} Returns the converted array. */ function asciiToArray(string) { return string.split(''); } /** * Splits an ASCII `string` into an array of its words. * * @private * @param {string} The string to inspect. * @returns {Array} Returns the words of `string`. */ function asciiWords(string) { return string.match(reAsciiWord) || []; } /** * The base implementation of methods like `_.findKey` and `_.findLastKey`, * without support for iteratee shorthands, which iterates over `collection` * using `eachFunc`. * * @private * @param {Array|Object} collection The collection to inspect. * @param {Function} predicate The function invoked per iteration. * @param {Function} eachFunc The function to iterate over `collection`. * @returns {*} Returns the found element or its key, else `undefined`. */ function baseFindKey(collection, predicate, eachFunc) { var result; eachFunc(collection, function(value, key, collection) { if (predicate(value, key, collection)) { result = key; return false; } }); return result; } /** * The base implementation of `_.findIndex` and `_.findLastIndex` without * support for iteratee shorthands. * * @private * @param {Array} array The array to inspect. * @param {Function} predicate The function invoked per iteration. * @param {number} fromIndex The index to search from. * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {number} Returns the index of the matched value, else `-1`. */ function baseFindIndex(array, predicate, fromIndex, fromRight) { var length = array.length, index = fromIndex + (fromRight ? 1 : -1); while ((fromRight ? index-- : ++index < length)) { if (predicate(array[index], index, array)) { return index; } } return -1; } /** * The base implementation of `_.indexOf` without `fromIndex` bounds checks. * * @private * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @param {number} fromIndex The index to search from. * @returns {number} Returns the index of the matched value, else `-1`. */ function baseIndexOf(array, value, fromIndex) { return value === value ? strictIndexOf(array, value, fromIndex) : baseFindIndex(array, baseIsNaN, fromIndex); } /** * This function is like `baseIndexOf` except that it accepts a comparator. * * @private * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @param {number} fromIndex The index to search from. * @param {Function} comparator The comparator invoked per element. * @returns {number} Returns the index of the matched value, else `-1`. */ function baseIndexOfWith(array, value, fromIndex, comparator) { var index = fromIndex - 1, length = array.length; while (++index < length) { if (comparator(array[index], value)) { return index; } } return -1; } /** * The base implementation of `_.isNaN` without support for number objects. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is `NaN`, else `false`. */ function baseIsNaN(value) { return value !== value; } /** * The base implementation of `_.mean` and `_.meanBy` without support for * iteratee shorthands. * * @private * @param {Array} array The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {number} Returns the mean. */ function baseMean(array, iteratee) { var length = array == null ? 0 : array.length; return length ? (baseSum(array, iteratee) / length) : NAN; } /** * The base implementation of `_.property` without support for deep paths. * * @private * @param {string} key The key of the property to get. * @returns {Function} Returns the new accessor function. */ function baseProperty(key) { return function(object) { return object == null ? undefined : object[key]; }; } /** * The base implementation of `_.propertyOf` without support for deep paths. * * @private * @param {Object} object The object to query. * @returns {Function} Returns the new accessor function. */ function basePropertyOf(object) { return function(key) { return object == null ? undefined : object[key]; }; } /** * The base implementation of `_.reduce` and `_.reduceRight`, without support * for iteratee shorthands, which iterates over `collection` using `eachFunc`. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} iteratee The function invoked per iteration. * @param {*} accumulator The initial value. * @param {boolean} initAccum Specify using the first or last element of * `collection` as the initial value. * @param {Function} eachFunc The function to iterate over `collection`. * @returns {*} Returns the accumulated value. */ function baseReduce(collection, iteratee, accumulator, initAccum, eachFunc) { eachFunc(collection, function(value, index, collection) { accumulator = initAccum ? (initAccum = false, value) : iteratee(accumulator, value, index, collection); }); return accumulator; } /** * The base implementation of `_.sortBy` which uses `comparer` to define the * sort order of `array` and replaces criteria objects with their corresponding * values. * * @private * @param {Array} array The array to sort. * @param {Function} comparer The function to define sort order. * @returns {Array} Returns `array`. */ function baseSortBy(array, comparer) { var length = array.length; array.sort(comparer); while (length--) { array[length] = array[length].value; } return array; } /** * The base implementation of `_.sum` and `_.sumBy` without support for * iteratee shorthands. * * @private * @param {Array} array The array to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {number} Returns the sum. */ function baseSum(array, iteratee) { var result, index = -1, length = array.length; while (++index < length) { var current = iteratee(array[index]); if (current !== undefined) { result = result === undefined ? current : (result + current); } } return result; } /** * The base implementation of `_.times` without support for iteratee shorthands * or max array length checks. * * @private * @param {number} n The number of times to invoke `iteratee`. * @param {Function} iteratee The function invoked per iteration. * @returns {Array} Returns the array of results. */ function baseTimes(n, iteratee) { var index = -1, result = Array(n); while (++index < n) { result[index] = iteratee(index); } return result; } /** * The base implementation of `_.toPairs` and `_.toPairsIn` which creates an array * of key-value pairs for `object` corresponding to the property names of `props`. * * @private * @param {Object} object The object to query. * @param {Array} props The property names to get values for. * @returns {Object} Returns the key-value pairs. */ function baseToPairs(object, props) { return arrayMap(props, function(key) { return [key, object[key]]; }); } /** * The base implementation of `_.unary` without support for storing metadata. * * @private * @param {Function} func The function to cap arguments for. * @returns {Function} Returns the new capped function. */ function baseUnary(func) { return function(value) { return func(value); }; } /** * The base implementation of `_.values` and `_.valuesIn` which creates an * array of `object` property values corresponding to the property names * of `props`. * * @private * @param {Object} object The object to query. * @param {Array} props The property names to get values for. * @returns {Object} Returns the array of property values. */ function baseValues(object, props) { return arrayMap(props, function(key) { return object[key]; }); } /** * Checks if a `cache` value for `key` exists. * * @private * @param {Object} cache The cache to query. * @param {string} key The key of the entry to check. * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. */ function cacheHas(cache, key) { return cache.has(key); } /** * Used by `_.trim` and `_.trimStart` to get the index of the first string symbol * that is not found in the character symbols. * * @private * @param {Array} strSymbols The string symbols to inspect. * @param {Array} chrSymbols The character symbols to find. * @returns {number} Returns the index of the first unmatched string symbol. */ function charsStartIndex(strSymbols, chrSymbols) { var index = -1, length = strSymbols.length; while (++index < length && baseIndexOf(chrSymbols, strSymbols[index], 0) > -1) {} return index; } /** * Used by `_.trim` and `_.trimEnd` to get the index of the last string symbol * that is not found in the character symbols. * * @private * @param {Array} strSymbols The string symbols to inspect. * @param {Array} chrSymbols The character symbols to find. * @returns {number} Returns the index of the last unmatched string symbol. */ function charsEndIndex(strSymbols, chrSymbols) { var index = strSymbols.length; while (index-- && baseIndexOf(chrSymbols, strSymbols[index], 0) > -1) {} return index; } /** * Gets the number of `placeholder` occurrences in `array`. * * @private * @param {Array} array The array to inspect. * @param {*} placeholder The placeholder to search for. * @returns {number} Returns the placeholder count. */ function countHolders(array, placeholder) { var length = array.length, result = 0; while (length--) { if (array[length] === placeholder) { ++result; } } return result; } /** * Used by `_.deburr` to convert Latin-1 Supplement and Latin Extended-A * letters to basic Latin letters. * * @private * @param {string} letter The matched letter to deburr. * @returns {string} Returns the deburred letter. */ var deburrLetter = basePropertyOf(deburredLetters); /** * Used by `_.escape` to convert characters to HTML entities. * * @private * @param {string} chr The matched character to escape. * @returns {string} Returns the escaped character. */ var escapeHtmlChar = basePropertyOf(htmlEscapes); /** * Used by `_.template` to escape characters for inclusion in compiled string literals. * * @private * @param {string} chr The matched character to escape. * @returns {string} Returns the escaped character. */ function escapeStringChar(chr) { return '\\' + stringEscapes[chr]; } /** * Gets the value at `key` of `object`. * * @private * @param {Object} [object] The object to query. * @param {string} key The key of the property to get. * @returns {*} Returns the property value. */ function getValue(object, key) { return object == null ? undefined : object[key]; } /** * Checks if `string` contains Unicode symbols. * * @private * @param {string} string The string to inspect. * @returns {boolean} Returns `true` if a symbol is found, else `false`. */ function hasUnicode(string) { return reHasUnicode.test(string); } /** * Checks if `string` contains a word composed of Unicode symbols. * * @private * @param {string} string The string to inspect. * @returns {boolean} Returns `true` if a word is found, else `false`. */ function hasUnicodeWord(string) { return reHasUnicodeWord.test(string); } /** * Converts `iterator` to an array. * * @private * @param {Object} iterator The iterator to convert. * @returns {Array} Returns the converted array. */ function iteratorToArray(iterator) { var data, result = []; while (!(data = iterator.next()).done) { result.push(data.value); } return result; } /** * Converts `map` to its key-value pairs. * * @private * @param {Object} map The map to convert. * @returns {Array} Returns the key-value pairs. */ function mapToArray(map) { var index = -1, result = Array(map.size); map.forEach(function(value, key) { result[++index] = [key, value]; }); return result; } /** * Creates a unary function that invokes `func` with its argument transformed. * * @private * @param {Function} func The function to wrap. * @param {Function} transform The argument transform. * @returns {Function} Returns the new function. */ function overArg(func, transform) { return function(arg) { return func(transform(arg)); }; } /** * Replaces all `placeholder` elements in `array` with an internal placeholder * and returns an array of their indexes. * * @private * @param {Array} array The array to modify. * @param {*} placeholder The placeholder to replace. * @returns {Array} Returns the new array of placeholder indexes. */ function replaceHolders(array, placeholder) { var index = -1, length = array.length, resIndex = 0, result = []; while (++index < length) { var value = array[index]; if (value === placeholder || value === PLACEHOLDER) { array[index] = PLACEHOLDER; result[resIndex++] = index; } } return result; } /** * Converts `set` to an array of its values. * * @private * @param {Object} set The set to convert. * @returns {Array} Returns the values. */ function setToArray(set) { var index = -1, result = Array(set.size); set.forEach(function(value) { result[++index] = value; }); return result; } /** * Converts `set` to its value-value pairs. * * @private * @param {Object} set The set to convert. * @returns {Array} Returns the value-value pairs. */ function setToPairs(set) { var index = -1, result = Array(set.size); set.forEach(function(value) { result[++index] = [value, value]; }); return result; } /** * A specialized version of `_.indexOf` which performs strict equality * comparisons of values, i.e. `===`. * * @private * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @param {number} fromIndex The index to search from. * @returns {number} Returns the index of the matched value, else `-1`. */ function strictIndexOf(array, value, fromIndex) { var index = fromIndex - 1, length = array.length; while (++index < length) { if (array[index] === value) { return index; } } return -1; } /** * A specialized version of `_.lastIndexOf` which performs strict equality * comparisons of values, i.e. `===`. * * @private * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @param {number} fromIndex The index to search from. * @returns {number} Returns the index of the matched value, else `-1`. */ function strictLastIndexOf(array, value, fromIndex) { var index = fromIndex + 1; while (index--) { if (array[index] === value) { return index; } } return index; } /** * Gets the number of symbols in `string`. * * @private * @param {string} string The string to inspect. * @returns {number} Returns the string size. */ function stringSize(string) { return hasUnicode(string) ? unicodeSize(string) : asciiSize(string); } /** * Converts `string` to an array. * * @private * @param {string} string The string to convert. * @returns {Array} Returns the converted array. */ function stringToArray(string) { return hasUnicode(string) ? unicodeToArray(string) : asciiToArray(string); } /** * Used by `_.unescape` to convert HTML entities to characters. * * @private * @param {string} chr The matched character to unescape. * @returns {string} Returns the unescaped character. */ var unescapeHtmlChar = basePropertyOf(htmlUnescapes); /** * Gets the size of a Unicode `string`. * * @private * @param {string} string The string inspect. * @returns {number} Returns the string size. */ function unicodeSize(string) { var result = reUnicode.lastIndex = 0; while (reUnicode.test(string)) { ++result; } return result; } /** * Converts a Unicode `string` to an array. * * @private * @param {string} string The string to convert. * @returns {Array} Returns the converted array. */ function unicodeToArray(string) { return string.match(reUnicode) || []; } /** * Splits a Unicode `string` into an array of its words. * * @private * @param {string} The string to inspect. * @returns {Array} Returns the words of `string`. */ function unicodeWords(string) { return string.match(reUnicodeWord) || []; } /*--------------------------------------------------------------------------*/ /** * Create a new pristine `lodash` function using the `context` object. * * @static * @memberOf _ * @since 1.1.0 * @category Util * @param {Object} [context=root] The context object. * @returns {Function} Returns a new `lodash` function. * @example * * _.mixin({ 'foo': _.constant('foo') }); * * var lodash = _.runInContext(); * lodash.mixin({ 'bar': lodash.constant('bar') }); * * _.isFunction(_.foo); * // => true * _.isFunction(_.bar); * // => false * * lodash.isFunction(lodash.foo); * // => false * lodash.isFunction(lodash.bar); * // => true * * // Create a suped-up `defer` in Node.js. * var defer = _.runInContext({ 'setTimeout': setImmediate }).defer; */ var runInContext = (function runInContext(context) { context = context == null ? root : _.defaults(root.Object(), context, _.pick(root, contextProps)); /** Built-in constructor references. */ var Array = context.Array, Date = context.Date, Error = context.Error, Function = context.Function, Math = context.Math, Object = context.Object, RegExp = context.RegExp, String = context.String, TypeError = context.TypeError; /** Used for built-in method references. */ var arrayProto = Array.prototype, funcProto = Function.prototype, objectProto = Object.prototype; /** Used to detect overreaching core-js shims. */ var coreJsData = context['__core-js_shared__']; /** Used to resolve the decompiled source of functions. */ var funcToString = funcProto.toString; /** Used to check objects for own properties. */ var hasOwnProperty = objectProto.hasOwnProperty; /** Used to generate unique IDs. */ var idCounter = 0; /** Used to detect methods masquerading as native. */ var maskSrcKey = (function() { var uid = /[^.]+$/.exec(coreJsData && coreJsData.keys && coreJsData.keys.IE_PROTO || ''); return uid ? ('Symbol(src)_1.' + uid) : ''; }()); /** * Used to resolve the * [`toStringTag`](http://ecma-international.org/ecma-262/7.0/#sec-object.prototype.tostring) * of values. */ var nativeObjectToString = objectProto.toString; /** Used to infer the `Object` constructor. */ var objectCtorString = funcToString.call(Object); /** Used to restore the original `_` reference in `_.noConflict`. */ var oldDash = root._; /** Used to detect if a method is native. */ var reIsNative = RegExp('^' + funcToString.call(hasOwnProperty).replace(reRegExpChar, '\\$&') .replace(/hasOwnProperty|(function).*?(?=\\\()| for .+?(?=\\\])/g, '$1.*?') + '$' ); /** Built-in value references. */ var Buffer = moduleExports ? context.Buffer : undefined, Symbol = context.Symbol, Uint8Array = context.Uint8Array, allocUnsafe = Buffer ? Buffer.allocUnsafe : undefined, getPrototype = overArg(Object.getPrototypeOf, Object), objectCreate = Object.create, propertyIsEnumerable = objectProto.propertyIsEnumerable, splice = arrayProto.splice, spreadableSymbol = Symbol ? Symbol.isConcatSpreadable : undefined, symIterator = Symbol ? Symbol.iterator : undefined, symToStringTag = Symbol ? Symbol.toStringTag : undefined; var defineProperty = (function() { try { var func = getNative(Object, 'defineProperty'); func({}, '', {}); return func; } catch (e) {} }()); /** Mocked built-ins. */ var ctxClearTimeout = context.clearTimeout !== root.clearTimeout && context.clearTimeout, ctxNow = Date && Date.now !== root.Date.now && Date.now, ctxSetTimeout = context.setTimeout !== root.setTimeout && context.setTimeout; /* Built-in method references for those with the same name as other `lodash` methods. */ var nativeCeil = Math.ceil, nativeFloor = Math.floor, nativeGetSymbols = Object.getOwnPropertySymbols, nativeIsBuffer = Buffer ? Buffer.isBuffer : undefined, nativeIsFinite = context.isFinite, nativeJoin = arrayProto.join, nativeKeys = overArg(Object.keys, Object), nativeMax = Math.max, nativeMin = Math.min, nativeNow = Date.now, nativeParseInt = context.parseInt, nativeRandom = Math.random, nativeReverse = arrayProto.reverse; /* Built-in method references that are verified to be native. */ var DataView = getNative(context, 'DataView'), Map = getNative(context, 'Map'), Promise = getNative(context, 'Promise'), Set = getNative(context, 'Set'), WeakMap = getNative(context, 'WeakMap'), nativeCreate = getNative(Object, 'create'); /** Used to store function metadata. */ var metaMap = WeakMap && new WeakMap; /** Used to lookup unminified function names. */ var realNames = {}; /** Used to detect maps, sets, and weakmaps. */ var dataViewCtorString = toSource(DataView), mapCtorString = toSource(Map), promiseCtorString = toSource(Promise), setCtorString = toSource(Set), weakMapCtorString = toSource(WeakMap); /** Used to convert symbols to primitives and strings. */ var symbolProto = Symbol ? Symbol.prototype : undefined, symbolValueOf = symbolProto ? symbolProto.valueOf : undefined, symbolToString = symbolProto ? symbolProto.toString : undefined; /*------------------------------------------------------------------------*/ /** * Creates a `lodash` object which wraps `value` to enable implicit method * chain sequences. Methods that operate on and return arrays, collections, * and functions can be chained together. Methods that retrieve a single value * or may return a primitive value will automatically end the chain sequence * and return the unwrapped value. Otherwise, the value must be unwrapped * with `_#value`. * * Explicit chain sequences, which must be unwrapped with `_#value`, may be * enabled using `_.chain`. * * The execution of chained methods is lazy, that is, it's deferred until * `_#value` is implicitly or explicitly called. * * Lazy evaluation allows several methods to support shortcut fusion. * Shortcut fusion is an optimization to merge iteratee calls; this avoids * the creation of intermediate arrays and can greatly reduce the number of * iteratee executions. Sections of a chain sequence qualify for shortcut * fusion if the section is applied to an array and iteratees accept only * one argument. The heuristic for whether a section qualifies for shortcut * fusion is subject to change. * * Chaining is supported in custom builds as long as the `_#value` method is * directly or indirectly included in the build. * * In addition to lodash methods, wrappers have `Array` and `String` methods. * * The wrapper `Array` methods are: * `concat`, `join`, `pop`, `push`, `shift`, `sort`, `splice`, and `unshift` * * The wrapper `String` methods are: * `replace` and `split` * * The wrapper methods that support shortcut fusion are: * `at`, `compact`, `drop`, `dropRight`, `dropWhile`, `filter`, `find`, * `findLast`, `head`, `initial`, `last`, `map`, `reject`, `reverse`, `slice`, * `tail`, `take`, `takeRight`, `takeRightWhile`, `takeWhile`, and `toArray` * * The chainable wrapper methods are: * `after`, `ary`, `assign`, `assignIn`, `assignInWith`, `assignWith`, `at`, * `before`, `bind`, `bindAll`, `bindKey`, `castArray`, `chain`, `chunk`, * `commit`, `compact`, `concat`, `conforms`, `constant`, `countBy`, `create`, * `curry`, `debounce`, `defaults`, `defaultsDeep`, `defer`, `delay`, * `difference`, `differenceBy`, `differenceWith`, `drop`, `dropRight`, * `dropRightWhile`, `dropWhile`, `extend`, `extendWith`, `fill`, `filter`, * `flatMap`, `flatMapDeep`, `flatMapDepth`, `flatten`, `flattenDeep`, * `flattenDepth`, `flip`, `flow`, `flowRight`, `fromPairs`, `functions`, * `functionsIn`, `groupBy`, `initial`, `intersection`, `intersectionBy`, * `intersectionWith`, `invert`, `invertBy`, `invokeMap`, `iteratee`, `keyBy`, * `keys`, `keysIn`, `map`, `mapKeys`, `mapValues`, `matches`, `matchesProperty`, * `memoize`, `merge`, `mergeWith`, `method`, `methodOf`, `mixin`, `negate`, * `nthArg`, `omit`, `omitBy`, `once`, `orderBy`, `over`, `overArgs`, * `overEvery`, `overSome`, `partial`, `partialRight`, `partition`, `pick`, * `pickBy`, `plant`, `property`, `propertyOf`, `pull`, `pullAll`, `pullAllBy`, * `pullAllWith`, `pullAt`, `push`, `range`, `rangeRight`, `rearg`, `reject`, * `remove`, `rest`, `reverse`, `sampleSize`, `set`, `setWith`, `shuffle`, * `slice`, `sort`, `sortBy`, `splice`, `spread`, `tail`, `take`, `takeRight`, * `takeRightWhile`, `takeWhile`, `tap`, `throttle`, `thru`, `toArray`, * `toPairs`, `toPairsIn`, `toPath`, `toPlainObject`, `transform`, `unary`, * `union`, `unionBy`, `unionWith`, `uniq`, `uniqBy`, `uniqWith`, `unset`, * `unshift`, `unzip`, `unzipWith`, `update`, `updateWith`, `values`, * `valuesIn`, `without`, `wrap`, `xor`, `xorBy`, `xorWith`, `zip`, * `zipObject`, `zipObjectDeep`, and `zipWith` * * The wrapper methods that are **not** chainable by default are: * `add`, `attempt`, `camelCase`, `capitalize`, `ceil`, `clamp`, `clone`, * `cloneDeep`, `cloneDeepWith`, `cloneWith`, `conformsTo`, `deburr`, * `defaultTo`, `divide`, `each`, `eachRight`, `endsWith`, `eq`, `escape`, * `escapeRegExp`, `every`, `find`, `findIndex`, `findKey`, `findLast`, * `findLastIndex`, `findLastKey`, `first`, `floor`, `forEach`, `forEachRight`, * `forIn`, `forInRight`, `forOwn`, `forOwnRight`, `get`, `gt`, `gte`, `has`, * `hasIn`, `head`, `identity`, `includes`, `indexOf`, `inRange`, `invoke`, * `isArguments`, `isArray`, `isArrayBuffer`, `isArrayLike`, `isArrayLikeObject`, * `isBoolean`, `isBuffer`, `isDate`, `isElement`, `isEmpty`, `isEqual`, * `isEqualWith`, `isError`, `isFinite`, `isFunction`, `isInteger`, `isLength`, * `isMap`, `isMatch`, `isMatchWith`, `isNaN`, `isNative`, `isNil`, `isNull`, * `isNumber`, `isObject`, `isObjectLike`, `isPlainObject`, `isRegExp`, * `isSafeInteger`, `isSet`, `isString`, `isUndefined`, `isTypedArray`, * `isWeakMap`, `isWeakSet`, `join`, `kebabCase`, `last`, `lastIndexOf`, * `lowerCase`, `lowerFirst`, `lt`, `lte`, `max`, `maxBy`, `mean`, `meanBy`, * `min`, `minBy`, `multiply`, `noConflict`, `noop`, `now`, `nth`, `pad`, * `padEnd`, `padStart`, `parseInt`, `pop`, `random`, `reduce`, `reduceRight`, * `repeat`, `result`, `round`, `runInContext`, `sample`, `shift`, `size`, * `snakeCase`, `some`, `sortedIndex`, `sortedIndexBy`, `sortedLastIndex`, * `sortedLastIndexBy`, `startCase`, `startsWith`, `stubArray`, `stubFalse`, * `stubObject`, `stubString`, `stubTrue`, `subtract`, `sum`, `sumBy`, * `template`, `times`, `toFinite`, `toInteger`, `toJSON`, `toLength`, * `toLower`, `toNumber`, `toSafeInteger`, `toString`, `toUpper`, `trim`, * `trimEnd`, `trimStart`, `truncate`, `unescape`, `uniqueId`, `upperCase`, * `upperFirst`, `value`, and `words` * * @name _ * @constructor * @category Seq * @param {*} value The value to wrap in a `lodash` instance. * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * function square(n) { * return n * n; * } * * var wrapped = _([1, 2, 3]); * * // Returns an unwrapped value. * wrapped.reduce(_.add); * // => 6 * * // Returns a wrapped value. * var squares = wrapped.map(square); * * _.isArray(squares); * // => false * * _.isArray(squares.value()); * // => true */ function lodash(value) { if (isObjectLike(value) && !isArray(value) && !(value instanceof LazyWrapper)) { if (value instanceof LodashWrapper) { return value; } if (hasOwnProperty.call(value, '__wrapped__')) { return wrapperClone(value); } } return new LodashWrapper(value); } /** * The base implementation of `_.create` without support for assigning * properties to the created object. * * @private * @param {Object} proto The object to inherit from. * @returns {Object} Returns the new object. */ var baseCreate = (function() { function object() {} return function(proto) { if (!isObject(proto)) { return {}; } if (objectCreate) { return objectCreate(proto); } object.prototype = proto; var result = new object; object.prototype = undefined; return result; }; }()); /** * The function whose prototype chain sequence wrappers inherit from. * * @private */ function baseLodash() { // No operation performed. } /** * The base constructor for creating `lodash` wrapper objects. * * @private * @param {*} value The value to wrap. * @param {boolean} [chainAll] Enable explicit method chain sequences. */ function LodashWrapper(value, chainAll) { this.__wrapped__ = value; this.__actions__ = []; this.__chain__ = !!chainAll; this.__index__ = 0; this.__values__ = undefined; } /** * By default, the template delimiters used by lodash are like those in * embedded Ruby (ERB) as well as ES2015 template strings. Change the * following template settings to use alternative delimiters. * * @static * @memberOf _ * @type {Object} */ lodash.templateSettings = { /** * Used to detect `data` property values to be HTML-escaped. * * @memberOf _.templateSettings * @type {RegExp} */ 'escape': reEscape, /** * Used to detect code to be evaluated. * * @memberOf _.templateSettings * @type {RegExp} */ 'evaluate': reEvaluate, /** * Used to detect `data` property values to inject. * * @memberOf _.templateSettings * @type {RegExp} */ 'interpolate': reInterpolate, /** * Used to reference the data object in the template text. * * @memberOf _.templateSettings * @type {string} */ 'variable': '', /** * Used to import variables into the compiled template. * * @memberOf _.templateSettings * @type {Object} */ 'imports': { /** * A reference to the `lodash` function. * * @memberOf _.templateSettings.imports * @type {Function} */ '_': lodash } }; // Ensure wrappers are instances of `baseLodash`. lodash.prototype = baseLodash.prototype; lodash.prototype.constructor = lodash; LodashWrapper.prototype = baseCreate(baseLodash.prototype); LodashWrapper.prototype.constructor = LodashWrapper; /*------------------------------------------------------------------------*/ /** * Creates a lazy wrapper object which wraps `value` to enable lazy evaluation. * * @private * @constructor * @param {*} value The value to wrap. */ function LazyWrapper(value) { this.__wrapped__ = value; this.__actions__ = []; this.__dir__ = 1; this.__filtered__ = false; this.__iteratees__ = []; this.__takeCount__ = MAX_ARRAY_LENGTH; this.__views__ = []; } /** * Creates a clone of the lazy wrapper object. * * @private * @name clone * @memberOf LazyWrapper * @returns {Object} Returns the cloned `LazyWrapper` object. */ function lazyClone() { var result = new LazyWrapper(this.__wrapped__); result.__actions__ = copyArray(this.__actions__); result.__dir__ = this.__dir__; result.__filtered__ = this.__filtered__; result.__iteratees__ = copyArray(this.__iteratees__); result.__takeCount__ = this.__takeCount__; result.__views__ = copyArray(this.__views__); return result; } /** * Reverses the direction of lazy iteration. * * @private * @name reverse * @memberOf LazyWrapper * @returns {Object} Returns the new reversed `LazyWrapper` object. */ function lazyReverse() { if (this.__filtered__) { var result = new LazyWrapper(this); result.__dir__ = -1; result.__filtered__ = true; } else { result = this.clone(); result.__dir__ *= -1; } return result; } /** * Extracts the unwrapped value from its lazy wrapper. * * @private * @name value * @memberOf LazyWrapper * @returns {*} Returns the unwrapped value. */ function lazyValue() { var array = this.__wrapped__.value(), dir = this.__dir__, isArr = isArray(array), isRight = dir < 0, arrLength = isArr ? array.length : 0, view = getView(0, arrLength, this.__views__), start = view.start, end = view.end, length = end - start, index = isRight ? end : (start - 1), iteratees = this.__iteratees__, iterLength = iteratees.length, resIndex = 0, takeCount = nativeMin(length, this.__takeCount__); if (!isArr || (!isRight && arrLength == length && takeCount == length)) { return baseWrapperValue(array, this.__actions__); } var result = []; outer: while (length-- && resIndex < takeCount) { index += dir; var iterIndex = -1, value = array[index]; while (++iterIndex < iterLength) { var data = iteratees[iterIndex], iteratee = data.iteratee, type = data.type, computed = iteratee(value); if (type == LAZY_MAP_FLAG) { value = computed; } else if (!computed) { if (type == LAZY_FILTER_FLAG) { continue outer; } else { break outer; } } } result[resIndex++] = value; } return result; } // Ensure `LazyWrapper` is an instance of `baseLodash`. LazyWrapper.prototype = baseCreate(baseLodash.prototype); LazyWrapper.prototype.constructor = LazyWrapper; /*------------------------------------------------------------------------*/ /** * Creates a hash object. * * @private * @constructor * @param {Array} [entries] The key-value pairs to cache. */ function Hash(entries) { var index = -1, length = entries == null ? 0 : entries.length; this.clear(); while (++index < length) { var entry = entries[index]; this.set(entry[0], entry[1]); } } /** * Removes all key-value entries from the hash. * * @private * @name clear * @memberOf Hash */ function hashClear() { this.__data__ = nativeCreate ? nativeCreate(null) : {}; this.size = 0; } /** * Removes `key` and its value from the hash. * * @private * @name delete * @memberOf Hash * @param {Object} hash The hash to modify. * @param {string} key The key of the value to remove. * @returns {boolean} Returns `true` if the entry was removed, else `false`. */ function hashDelete(key) { var result = this.has(key) && delete this.__data__[key]; this.size -= result ? 1 : 0; return result; } /** * Gets the hash value for `key`. * * @private * @name get * @memberOf Hash * @param {string} key The key of the value to get. * @returns {*} Returns the entry value. */ function hashGet(key) { var data = this.__data__; if (nativeCreate) { var result = data[key]; return result === HASH_UNDEFINED ? undefined : result; } return hasOwnProperty.call(data, key) ? data[key] : undefined; } /** * Checks if a hash value for `key` exists. * * @private * @name has * @memberOf Hash * @param {string} key The key of the entry to check. * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. */ function hashHas(key) { var data = this.__data__; return nativeCreate ? (data[key] !== undefined) : hasOwnProperty.call(data, key); } /** * Sets the hash `key` to `value`. * * @private * @name set * @memberOf Hash * @param {string} key The key of the value to set. * @param {*} value The value to set. * @returns {Object} Returns the hash instance. */ function hashSet(key, value) { var data = this.__data__; this.size += this.has(key) ? 0 : 1; data[key] = (nativeCreate && value === undefined) ? HASH_UNDEFINED : value; return this; } // Add methods to `Hash`. Hash.prototype.clear = hashClear; Hash.prototype['delete'] = hashDelete; Hash.prototype.get = hashGet; Hash.prototype.has = hashHas; Hash.prototype.set = hashSet; /*------------------------------------------------------------------------*/ /** * Creates an list cache object. * * @private * @constructor * @param {Array} [entries] The key-value pairs to cache. */ function ListCache(entries) { var index = -1, length = entries == null ? 0 : entries.length; this.clear(); while (++index < length) { var entry = entries[index]; this.set(entry[0], entry[1]); } } /** * Removes all key-value entries from the list cache. * * @private * @name clear * @memberOf ListCache */ function listCacheClear() { this.__data__ = []; this.size = 0; } /** * Removes `key` and its value from the list cache. * * @private * @name delete * @memberOf ListCache * @param {string} key The key of the value to remove. * @returns {boolean} Returns `true` if the entry was removed, else `false`. */ function listCacheDelete(key) { var data = this.__data__, index = assocIndexOf(data, key); if (index < 0) { return false; } var lastIndex = data.length - 1; if (index == lastIndex) { data.pop(); } else { splice.call(data, index, 1); } --this.size; return true; } /** * Gets the list cache value for `key`. * * @private * @name get * @memberOf ListCache * @param {string} key The key of the value to get. * @returns {*} Returns the entry value. */ function listCacheGet(key) { var data = this.__data__, index = assocIndexOf(data, key); return index < 0 ? undefined : data[index][1]; } /** * Checks if a list cache value for `key` exists. * * @private * @name has * @memberOf ListCache * @param {string} key The key of the entry to check. * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. */ function listCacheHas(key) { return assocIndexOf(this.__data__, key) > -1; } /** * Sets the list cache `key` to `value`. * * @private * @name set * @memberOf ListCache * @param {string} key The key of the value to set. * @param {*} value The value to set. * @returns {Object} Returns the list cache instance. */ function listCacheSet(key, value) { var data = this.__data__, index = assocIndexOf(data, key); if (index < 0) { ++this.size; data.push([key, value]); } else { data[index][1] = value; } return this; } // Add methods to `ListCache`. ListCache.prototype.clear = listCacheClear; ListCache.prototype['delete'] = listCacheDelete; ListCache.prototype.get = listCacheGet; ListCache.prototype.has = listCacheHas; ListCache.prototype.set = listCacheSet; /*------------------------------------------------------------------------*/ /** * Creates a map cache object to store key-value pairs. * * @private * @constructor * @param {Array} [entries] The key-value pairs to cache. */ function MapCache(entries) { var index = -1, length = entries == null ? 0 : entries.length; this.clear(); while (++index < length) { var entry = entries[index]; this.set(entry[0], entry[1]); } } /** * Removes all key-value entries from the map. * * @private * @name clear * @memberOf MapCache */ function mapCacheClear() { this.size = 0; this.__data__ = { 'hash': new Hash, 'map': new (Map || ListCache), 'string': new Hash }; } /** * Removes `key` and its value from the map. * * @private * @name delete * @memberOf MapCache * @param {string} key The key of the value to remove. * @returns {boolean} Returns `true` if the entry was removed, else `false`. */ function mapCacheDelete(key) { var result = getMapData(this, key)['delete'](key); this.size -= result ? 1 : 0; return result; } /** * Gets the map value for `key`. * * @private * @name get * @memberOf MapCache * @param {string} key The key of the value to get. * @returns {*} Returns the entry value. */ function mapCacheGet(key) { return getMapData(this, key).get(key); } /** * Checks if a map value for `key` exists. * * @private * @name has * @memberOf MapCache * @param {string} key The key of the entry to check. * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. */ function mapCacheHas(key) { return getMapData(this, key).has(key); } /** * Sets the map `key` to `value`. * * @private * @name set * @memberOf MapCache * @param {string} key The key of the value to set. * @param {*} value The value to set. * @returns {Object} Returns the map cache instance. */ function mapCacheSet(key, value) { var data = getMapData(this, key), size = data.size; data.set(key, value); this.size += data.size == size ? 0 : 1; return this; } // Add methods to `MapCache`. MapCache.prototype.clear = mapCacheClear; MapCache.prototype['delete'] = mapCacheDelete; MapCache.prototype.get = mapCacheGet; MapCache.prototype.has = mapCacheHas; MapCache.prototype.set = mapCacheSet; /*------------------------------------------------------------------------*/ /** * * Creates an array cache object to store unique values. * * @private * @constructor * @param {Array} [values] The values to cache. */ function SetCache(values) { var index = -1, length = values == null ? 0 : values.length; this.__data__ = new MapCache; while (++index < length) { this.add(values[index]); } } /** * Adds `value` to the array cache. * * @private * @name add * @memberOf SetCache * @alias push * @param {*} value The value to cache. * @returns {Object} Returns the cache instance. */ function setCacheAdd(value) { this.__data__.set(value, HASH_UNDEFINED); return this; } /** * Checks if `value` is in the array cache. * * @private * @name has * @memberOf SetCache * @param {*} value The value to search for. * @returns {number} Returns `true` if `value` is found, else `false`. */ function setCacheHas(value) { return this.__data__.has(value); } // Add methods to `SetCache`. SetCache.prototype.add = SetCache.prototype.push = setCacheAdd; SetCache.prototype.has = setCacheHas; /*------------------------------------------------------------------------*/ /** * Creates a stack cache object to store key-value pairs. * * @private * @constructor * @param {Array} [entries] The key-value pairs to cache. */ function Stack(entries) { var data = this.__data__ = new ListCache(entries); this.size = data.size; } /** * Removes all key-value entries from the stack. * * @private * @name clear * @memberOf Stack */ function stackClear() { this.__data__ = new ListCache; this.size = 0; } /** * Removes `key` and its value from the stack. * * @private * @name delete * @memberOf Stack * @param {string} key The key of the value to remove. * @returns {boolean} Returns `true` if the entry was removed, else `false`. */ function stackDelete(key) { var data = this.__data__, result = data['delete'](key); this.size = data.size; return result; } /** * Gets the stack value for `key`. * * @private * @name get * @memberOf Stack * @param {string} key The key of the value to get. * @returns {*} Returns the entry value. */ function stackGet(key) { return this.__data__.get(key); } /** * Checks if a stack value for `key` exists. * * @private * @name has * @memberOf Stack * @param {string} key The key of the entry to check. * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. */ function stackHas(key) { return this.__data__.has(key); } /** * Sets the stack `key` to `value`. * * @private * @name set * @memberOf Stack * @param {string} key The key of the value to set. * @param {*} value The value to set. * @returns {Object} Returns the stack cache instance. */ function stackSet(key, value) { var data = this.__data__; if (data instanceof ListCache) { var pairs = data.__data__; if (!Map || (pairs.length < LARGE_ARRAY_SIZE - 1)) { pairs.push([key, value]); this.size = ++data.size; return this; } data = this.__data__ = new MapCache(pairs); } data.set(key, value); this.size = data.size; return this; } // Add methods to `Stack`. Stack.prototype.clear = stackClear; Stack.prototype['delete'] = stackDelete; Stack.prototype.get = stackGet; Stack.prototype.has = stackHas; Stack.prototype.set = stackSet; /*------------------------------------------------------------------------*/ /** * Creates an array of the enumerable property names of the array-like `value`. * * @private * @param {*} value The value to query. * @param {boolean} inherited Specify returning inherited property names. * @returns {Array} Returns the array of property names. */ function arrayLikeKeys(value, inherited) { var isArr = isArray(value), isArg = !isArr && isArguments(value), isBuff = !isArr && !isArg && isBuffer(value), isType = !isArr && !isArg && !isBuff && isTypedArray(value), skipIndexes = isArr || isArg || isBuff || isType, result = skipIndexes ? baseTimes(value.length, String) : [], length = result.length; for (var key in value) { if ((inherited || hasOwnProperty.call(value, key)) && !(skipIndexes && ( // Safari 9 has enumerable `arguments.length` in strict mode. key == 'length' || // Node.js 0.10 has enumerable non-index properties on buffers. (isBuff && (key == 'offset' || key == 'parent')) || // PhantomJS 2 has enumerable non-index properties on typed arrays. (isType && (key == 'buffer' || key == 'byteLength' || key == 'byteOffset')) || // Skip index properties. isIndex(key, length) ))) { result.push(key); } } return result; } /** * A specialized version of `_.sample` for arrays. * * @private * @param {Array} array The array to sample. * @returns {*} Returns the random element. */ function arraySample(array) { var length = array.length; return length ? array[baseRandom(0, length - 1)] : undefined; } /** * A specialized version of `_.sampleSize` for arrays. * * @private * @param {Array} array The array to sample. * @param {number} n The number of elements to sample. * @returns {Array} Returns the random elements. */ function arraySampleSize(array, n) { return shuffleSelf(copyArray(array), baseClamp(n, 0, array.length)); } /** * A specialized version of `_.shuffle` for arrays. * * @private * @param {Array} array The array to shuffle. * @returns {Array} Returns the new shuffled array. */ function arrayShuffle(array) { return shuffleSelf(copyArray(array)); } /** * This function is like `assignValue` except that it doesn't assign * `undefined` values. * * @private * @param {Object} object The object to modify. * @param {string} key The key of the property to assign. * @param {*} value The value to assign. */ function assignMergeValue(object, key, value) { if ((value !== undefined && !eq(object[key], value)) || (value === undefined && !(key in object))) { baseAssignValue(object, key, value); } } /** * Assigns `value` to `key` of `object` if the existing value is not equivalent * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. * * @private * @param {Object} object The object to modify. * @param {string} key The key of the property to assign. * @param {*} value The value to assign. */ function assignValue(object, key, value) { var objValue = object[key]; if (!(hasOwnProperty.call(object, key) && eq(objValue, value)) || (value === undefined && !(key in object))) { baseAssignValue(object, key, value); } } /** * Gets the index at which the `key` is found in `array` of key-value pairs. * * @private * @param {Array} array The array to inspect. * @param {*} key The key to search for. * @returns {number} Returns the index of the matched value, else `-1`. */ function assocIndexOf(array, key) { var length = array.length; while (length--) { if (eq(array[length][0], key)) { return length; } } return -1; } /** * Aggregates elements of `collection` on `accumulator` with keys transformed * by `iteratee` and values set by `setter`. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} setter The function to set `accumulator` values. * @param {Function} iteratee The iteratee to transform keys. * @param {Object} accumulator The initial aggregated object. * @returns {Function} Returns `accumulator`. */ function baseAggregator(collection, setter, iteratee, accumulator) { baseEach(collection, function(value, key, collection) { setter(accumulator, value, iteratee(value), collection); }); return accumulator; } /** * The base implementation of `_.assign` without support for multiple sources * or `customizer` functions. * * @private * @param {Object} object The destination object. * @param {Object} source The source object. * @returns {Object} Returns `object`. */ function baseAssign(object, source) { return object && copyObject(source, keys(source), object); } /** * The base implementation of `_.assignIn` without support for multiple sources * or `customizer` functions. * * @private * @param {Object} object The destination object. * @param {Object} source The source object. * @returns {Object} Returns `object`. */ function baseAssignIn(object, source) { return object && copyObject(source, keysIn(source), object); } /** * The base implementation of `assignValue` and `assignMergeValue` without * value checks. * * @private * @param {Object} object The object to modify. * @param {string} key The key of the property to assign. * @param {*} value The value to assign. */ function baseAssignValue(object, key, value) { if (key == '__proto__' && defineProperty) { defineProperty(object, key, { 'configurable': true, 'enumerable': true, 'value': value, 'writable': true }); } else { object[key] = value; } } /** * The base implementation of `_.at` without support for individual paths. * * @private * @param {Object} object The object to iterate over. * @param {string[]} paths The property paths to pick. * @returns {Array} Returns the picked elements. */ function baseAt(object, paths) { var index = -1, length = paths.length, result = Array(length), skip = object == null; while (++index < length) { result[index] = skip ? undefined : get(object, paths[index]); } return result; } /** * The base implementation of `_.clamp` which doesn't coerce arguments. * * @private * @param {number} number The number to clamp. * @param {number} [lower] The lower bound. * @param {number} upper The upper bound. * @returns {number} Returns the clamped number. */ function baseClamp(number, lower, upper) { if (number === number) { if (upper !== undefined) { number = number <= upper ? number : upper; } if (lower !== undefined) { number = number >= lower ? number : lower; } } return number; } /** * The base implementation of `_.clone` and `_.cloneDeep` which tracks * traversed objects. * * @private * @param {*} value The value to clone. * @param {boolean} bitmask The bitmask flags. * 1 - Deep clone * 2 - Flatten inherited properties * 4 - Clone symbols * @param {Function} [customizer] The function to customize cloning. * @param {string} [key] The key of `value`. * @param {Object} [object] The parent object of `value`. * @param {Object} [stack] Tracks traversed objects and their clone counterparts. * @returns {*} Returns the cloned value. */ function baseClone(value, bitmask, customizer, key, object, stack) { var result, isDeep = bitmask & CLONE_DEEP_FLAG, isFlat = bitmask & CLONE_FLAT_FLAG, isFull = bitmask & CLONE_SYMBOLS_FLAG; if (customizer) { result = object ? customizer(value, key, object, stack) : customizer(value); } if (result !== undefined) { return result; } if (!isObject(value)) { return value; } var isArr = isArray(value); if (isArr) { result = initCloneArray(value); if (!isDeep) { return copyArray(value, result); } } else { var tag = getTag(value), isFunc = tag == funcTag || tag == genTag; if (isBuffer(value)) { return cloneBuffer(value, isDeep); } if (tag == objectTag || tag == argsTag || (isFunc && !object)) { result = (isFlat || isFunc) ? {} : initCloneObject(value); if (!isDeep) { return isFlat ? copySymbolsIn(value, baseAssignIn(result, value)) : copySymbols(value, baseAssign(result, value)); } } else { if (!cloneableTags[tag]) { return object ? value : {}; } result = initCloneByTag(value, tag, baseClone, isDeep); } } // Check for circular references and return its corresponding clone. stack || (stack = new Stack); var stacked = stack.get(value); if (stacked) { return stacked; } stack.set(value, result); var keysFunc = isFull ? (isFlat ? getAllKeysIn : getAllKeys) : (isFlat ? keysIn : keys); var props = isArr ? undefined : keysFunc(value); arrayEach(props || value, function(subValue, key) { if (props) { key = subValue; subValue = value[key]; } // Recursively populate clone (susceptible to call stack limits). assignValue(result, key, baseClone(subValue, bitmask, customizer, key, value, stack)); }); return result; } /** * The base implementation of `_.conforms` which doesn't clone `source`. * * @private * @param {Object} source The object of property predicates to conform to. * @returns {Function} Returns the new spec function. */ function baseConforms(source) { var props = keys(source); return function(object) { return baseConformsTo(object, source, props); }; } /** * The base implementation of `_.conformsTo` which accepts `props` to check. * * @private * @param {Object} object The object to inspect. * @param {Object} source The object of property predicates to conform to. * @returns {boolean} Returns `true` if `object` conforms, else `false`. */ function baseConformsTo(object, source, props) { var length = props.length; if (object == null) { return !length; } object = Object(object); while (length--) { var key = props[length], predicate = source[key], value = object[key]; if ((value === undefined && !(key in object)) || !predicate(value)) { return false; } } return true; } /** * The base implementation of `_.delay` and `_.defer` which accepts `args` * to provide to `func`. * * @private * @param {Function} func The function to delay. * @param {number} wait The number of milliseconds to delay invocation. * @param {Array} args The arguments to provide to `func`. * @returns {number|Object} Returns the timer id or timeout object. */ function baseDelay(func, wait, args) { if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } return setTimeout(function() { func.apply(undefined, args); }, wait); } /** * The base implementation of methods like `_.difference` without support * for excluding multiple arrays or iteratee shorthands. * * @private * @param {Array} array The array to inspect. * @param {Array} values The values to exclude. * @param {Function} [iteratee] The iteratee invoked per element. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of filtered values. */ function baseDifference(array, values, iteratee, comparator) { var index = -1, includes = arrayIncludes, isCommon = true, length = array.length, result = [], valuesLength = values.length; if (!length) { return result; } if (iteratee) { values = arrayMap(values, baseUnary(iteratee)); } if (comparator) { includes = arrayIncludesWith; isCommon = false; } else if (values.length >= LARGE_ARRAY_SIZE) { includes = cacheHas; isCommon = false; values = new SetCache(values); } outer: while (++index < length) { var value = array[index], computed = iteratee == null ? value : iteratee(value); value = (comparator || value !== 0) ? value : 0; if (isCommon && computed === computed) { var valuesIndex = valuesLength; while (valuesIndex--) { if (values[valuesIndex] === computed) { continue outer; } } result.push(value); } else if (!includes(values, computed, comparator)) { result.push(value); } } return result; } /** * The base implementation of `_.forEach` without support for iteratee shorthands. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Array|Object} Returns `collection`. */ var baseEach = createBaseEach(baseForOwn); /** * The base implementation of `_.forEachRight` without support for iteratee shorthands. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Array|Object} Returns `collection`. */ var baseEachRight = createBaseEach(baseForOwnRight, true); /** * The base implementation of `_.every` without support for iteratee shorthands. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} predicate The function invoked per iteration. * @returns {boolean} Returns `true` if all elements pass the predicate check, * else `false` */ function baseEvery(collection, predicate) { var result = true; baseEach(collection, function(value, index, collection) { result = !!predicate(value, index, collection); return result; }); return result; } /** * The base implementation of methods like `_.max` and `_.min` which accepts a * `comparator` to determine the extremum value. * * @private * @param {Array} array The array to iterate over. * @param {Function} iteratee The iteratee invoked per iteration. * @param {Function} comparator The comparator used to compare values. * @returns {*} Returns the extremum value. */ function baseExtremum(array, iteratee, comparator) { var index = -1, length = array.length; while (++index < length) { var value = array[index], current = iteratee(value); if (current != null && (computed === undefined ? (current === current && !isSymbol(current)) : comparator(current, computed) )) { var computed = current, result = value; } } return result; } /** * The base implementation of `_.fill` without an iteratee call guard. * * @private * @param {Array} array The array to fill. * @param {*} value The value to fill `array` with. * @param {number} [start=0] The start position. * @param {number} [end=array.length] The end position. * @returns {Array} Returns `array`. */ function baseFill(array, value, start, end) { var length = array.length; start = toInteger(start); if (start < 0) { start = -start > length ? 0 : (length + start); } end = (end === undefined || end > length) ? length : toInteger(end); if (end < 0) { end += length; } end = start > end ? 0 : toLength(end); while (start < end) { array[start++] = value; } return array; } /** * The base implementation of `_.filter` without support for iteratee shorthands. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} predicate The function invoked per iteration. * @returns {Array} Returns the new filtered array. */ function baseFilter(collection, predicate) { var result = []; baseEach(collection, function(value, index, collection) { if (predicate(value, index, collection)) { result.push(value); } }); return result; } /** * The base implementation of `_.flatten` with support for restricting flattening. * * @private * @param {Array} array The array to flatten. * @param {number} depth The maximum recursion depth. * @param {boolean} [predicate=isFlattenable] The function invoked per iteration. * @param {boolean} [isStrict] Restrict to values that pass `predicate` checks. * @param {Array} [result=[]] The initial result value. * @returns {Array} Returns the new flattened array. */ function baseFlatten(array, depth, predicate, isStrict, result) { var index = -1, length = array.length; predicate || (predicate = isFlattenable); result || (result = []); while (++index < length) { var value = array[index]; if (depth > 0 && predicate(value)) { if (depth > 1) { // Recursively flatten arrays (susceptible to call stack limits). baseFlatten(value, depth - 1, predicate, isStrict, result); } else { arrayPush(result, value); } } else if (!isStrict) { result[result.length] = value; } } return result; } /** * The base implementation of `baseForOwn` which iterates over `object` * properties returned by `keysFunc` and invokes `iteratee` for each property. * Iteratee functions may exit iteration early by explicitly returning `false`. * * @private * @param {Object} object The object to iterate over. * @param {Function} iteratee The function invoked per iteration. * @param {Function} keysFunc The function to get the keys of `object`. * @returns {Object} Returns `object`. */ var baseFor = createBaseFor(); /** * This function is like `baseFor` except that it iterates over properties * in the opposite order. * * @private * @param {Object} object The object to iterate over. * @param {Function} iteratee The function invoked per iteration. * @param {Function} keysFunc The function to get the keys of `object`. * @returns {Object} Returns `object`. */ var baseForRight = createBaseFor(true); /** * The base implementation of `_.forOwn` without support for iteratee shorthands. * * @private * @param {Object} object The object to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Object} Returns `object`. */ function baseForOwn(object, iteratee) { return object && baseFor(object, iteratee, keys); } /** * The base implementation of `_.forOwnRight` without support for iteratee shorthands. * * @private * @param {Object} object The object to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Object} Returns `object`. */ function baseForOwnRight(object, iteratee) { return object && baseForRight(object, iteratee, keys); } /** * The base implementation of `_.functions` which creates an array of * `object` function property names filtered from `props`. * * @private * @param {Object} object The object to inspect. * @param {Array} props The property names to filter. * @returns {Array} Returns the function names. */ function baseFunctions(object, props) { return arrayFilter(props, function(key) { return isFunction(object[key]); }); } /** * The base implementation of `_.get` without support for default values. * * @private * @param {Object} object The object to query. * @param {Array|string} path The path of the property to get. * @returns {*} Returns the resolved value. */ function baseGet(object, path) { path = castPath(path, object); var index = 0, length = path.length; while (object != null && index < length) { object = object[toKey(path[index++])]; } return (index && index == length) ? object : undefined; } /** * The base implementation of `getAllKeys` and `getAllKeysIn` which uses * `keysFunc` and `symbolsFunc` to get the enumerable property names and * symbols of `object`. * * @private * @param {Object} object The object to query. * @param {Function} keysFunc The function to get the keys of `object`. * @param {Function} symbolsFunc The function to get the symbols of `object`. * @returns {Array} Returns the array of property names and symbols. */ function baseGetAllKeys(object, keysFunc, symbolsFunc) { var result = keysFunc(object); return isArray(object) ? result : arrayPush(result, symbolsFunc(object)); } /** * The base implementation of `getTag` without fallbacks for buggy environments. * * @private * @param {*} value The value to query. * @returns {string} Returns the `toStringTag`. */ function baseGetTag(value) { if (value == null) { return value === undefined ? undefinedTag : nullTag; } return (symToStringTag && symToStringTag in Object(value)) ? getRawTag(value) : objectToString(value); } /** * The base implementation of `_.gt` which doesn't coerce arguments. * * @private * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if `value` is greater than `other`, * else `false`. */ function baseGt(value, other) { return value > other; } /** * The base implementation of `_.has` without support for deep paths. * * @private * @param {Object} [object] The object to query. * @param {Array|string} key The key to check. * @returns {boolean} Returns `true` if `key` exists, else `false`. */ function baseHas(object, key) { return object != null && hasOwnProperty.call(object, key); } /** * The base implementation of `_.hasIn` without support for deep paths. * * @private * @param {Object} [object] The object to query. * @param {Array|string} key The key to check. * @returns {boolean} Returns `true` if `key` exists, else `false`. */ function baseHasIn(object, key) { return object != null && key in Object(object); } /** * The base implementation of `_.inRange` which doesn't coerce arguments. * * @private * @param {number} number The number to check. * @param {number} start The start of the range. * @param {number} end The end of the range. * @returns {boolean} Returns `true` if `number` is in the range, else `false`. */ function baseInRange(number, start, end) { return number >= nativeMin(start, end) && number < nativeMax(start, end); } /** * The base implementation of methods like `_.intersection`, without support * for iteratee shorthands, that accepts an array of arrays to inspect. * * @private * @param {Array} arrays The arrays to inspect. * @param {Function} [iteratee] The iteratee invoked per element. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of shared values. */ function baseIntersection(arrays, iteratee, comparator) { var includes = comparator ? arrayIncludesWith : arrayIncludes, length = arrays[0].length, othLength = arrays.length, othIndex = othLength, caches = Array(othLength), maxLength = Infinity, result = []; while (othIndex--) { var array = arrays[othIndex]; if (othIndex && iteratee) { array = arrayMap(array, baseUnary(iteratee)); } maxLength = nativeMin(array.length, maxLength); caches[othIndex] = !comparator && (iteratee || (length >= 120 && array.length >= 120)) ? new SetCache(othIndex && array) : undefined; } array = arrays[0]; var index = -1, seen = caches[0]; outer: while (++index < length && result.length < maxLength) { var value = array[index], computed = iteratee ? iteratee(value) : value; value = (comparator || value !== 0) ? value : 0; if (!(seen ? cacheHas(seen, computed) : includes(result, computed, comparator) )) { othIndex = othLength; while (--othIndex) { var cache = caches[othIndex]; if (!(cache ? cacheHas(cache, computed) : includes(arrays[othIndex], computed, comparator)) ) { continue outer; } } if (seen) { seen.push(computed); } result.push(value); } } return result; } /** * The base implementation of `_.invert` and `_.invertBy` which inverts * `object` with values transformed by `iteratee` and set by `setter`. * * @private * @param {Object} object The object to iterate over. * @param {Function} setter The function to set `accumulator` values. * @param {Function} iteratee The iteratee to transform values. * @param {Object} accumulator The initial inverted object. * @returns {Function} Returns `accumulator`. */ function baseInverter(object, setter, iteratee, accumulator) { baseForOwn(object, function(value, key, object) { setter(accumulator, iteratee(value), key, object); }); return accumulator; } /** * The base implementation of `_.invoke` without support for individual * method arguments. * * @private * @param {Object} object The object to query. * @param {Array|string} path The path of the method to invoke. * @param {Array} args The arguments to invoke the method with. * @returns {*} Returns the result of the invoked method. */ function baseInvoke(object, path, args) { path = castPath(path, object); object = parent(object, path); var func = object == null ? object : object[toKey(last(path))]; return func == null ? undefined : apply(func, object, args); } /** * The base implementation of `_.isArguments`. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an `arguments` object, */ function baseIsArguments(value) { return isObjectLike(value) && baseGetTag(value) == argsTag; } /** * The base implementation of `_.isArrayBuffer` without Node.js optimizations. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an array buffer, else `false`. */ function baseIsArrayBuffer(value) { return isObjectLike(value) && baseGetTag(value) == arrayBufferTag; } /** * The base implementation of `_.isDate` without Node.js optimizations. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a date object, else `false`. */ function baseIsDate(value) { return isObjectLike(value) && baseGetTag(value) == dateTag; } /** * The base implementation of `_.isEqual` which supports partial comparisons * and tracks traversed objects. * * @private * @param {*} value The value to compare. * @param {*} other The other value to compare. * @param {boolean} bitmask The bitmask flags. * 1 - Unordered comparison * 2 - Partial comparison * @param {Function} [customizer] The function to customize comparisons. * @param {Object} [stack] Tracks traversed `value` and `other` objects. * @returns {boolean} Returns `true` if the values are equivalent, else `false`. */ function baseIsEqual(value, other, bitmask, customizer, stack) { if (value === other) { return true; } if (value == null || other == null || (!isObjectLike(value) && !isObjectLike(other))) { return value !== value && other !== other; } return baseIsEqualDeep(value, other, bitmask, customizer, baseIsEqual, stack); } /** * A specialized version of `baseIsEqual` for arrays and objects which performs * deep comparisons and tracks traversed objects enabling objects with circular * references to be compared. * * @private * @param {Object} object The object to compare. * @param {Object} other The other object to compare. * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. * @param {Function} customizer The function to customize comparisons. * @param {Function} equalFunc The function to determine equivalents of values. * @param {Object} [stack] Tracks traversed `object` and `other` objects. * @returns {boolean} Returns `true` if the objects are equivalent, else `false`. */ function baseIsEqualDeep(object, other, bitmask, customizer, equalFunc, stack) { var objIsArr = isArray(object), othIsArr = isArray(other), objTag = objIsArr ? arrayTag : getTag(object), othTag = othIsArr ? arrayTag : getTag(other); objTag = objTag == argsTag ? objectTag : objTag; othTag = othTag == argsTag ? objectTag : othTag; var objIsObj = objTag == objectTag, othIsObj = othTag == objectTag, isSameTag = objTag == othTag; if (isSameTag && isBuffer(object)) { if (!isBuffer(other)) { return false; } objIsArr = true; objIsObj = false; } if (isSameTag && !objIsObj) { stack || (stack = new Stack); return (objIsArr || isTypedArray(object)) ? equalArrays(object, other, bitmask, customizer, equalFunc, stack) : equalByTag(object, other, objTag, bitmask, customizer, equalFunc, stack); } if (!(bitmask & COMPARE_PARTIAL_FLAG)) { var objIsWrapped = objIsObj && hasOwnProperty.call(object, '__wrapped__'), othIsWrapped = othIsObj && hasOwnProperty.call(other, '__wrapped__'); if (objIsWrapped || othIsWrapped) { var objUnwrapped = objIsWrapped ? object.value() : object, othUnwrapped = othIsWrapped ? other.value() : other; stack || (stack = new Stack); return equalFunc(objUnwrapped, othUnwrapped, bitmask, customizer, stack); } } if (!isSameTag) { return false; } stack || (stack = new Stack); return equalObjects(object, other, bitmask, customizer, equalFunc, stack); } /** * The base implementation of `_.isMap` without Node.js optimizations. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a map, else `false`. */ function baseIsMap(value) { return isObjectLike(value) && getTag(value) == mapTag; } /** * The base implementation of `_.isMatch` without support for iteratee shorthands. * * @private * @param {Object} object The object to inspect. * @param {Object} source The object of property values to match. * @param {Array} matchData The property names, values, and compare flags to match. * @param {Function} [customizer] The function to customize comparisons. * @returns {boolean} Returns `true` if `object` is a match, else `false`. */ function baseIsMatch(object, source, matchData, customizer) { var index = matchData.length, length = index, noCustomizer = !customizer; if (object == null) { return !length; } object = Object(object); while (index--) { var data = matchData[index]; if ((noCustomizer && data[2]) ? data[1] !== object[data[0]] : !(data[0] in object) ) { return false; } } while (++index < length) { data = matchData[index]; var key = data[0], objValue = object[key], srcValue = data[1]; if (noCustomizer && data[2]) { if (objValue === undefined && !(key in object)) { return false; } } else { var stack = new Stack; if (customizer) { var result = customizer(objValue, srcValue, key, object, source, stack); } if (!(result === undefined ? baseIsEqual(srcValue, objValue, COMPARE_PARTIAL_FLAG | COMPARE_UNORDERED_FLAG, customizer, stack) : result )) { return false; } } } return true; } /** * The base implementation of `_.isNative` without bad shim checks. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a native function, * else `false`. */ function baseIsNative(value) { if (!isObject(value) || isMasked(value)) { return false; } var pattern = isFunction(value) ? reIsNative : reIsHostCtor; return pattern.test(toSource(value)); } /** * The base implementation of `_.isRegExp` without Node.js optimizations. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a regexp, else `false`. */ function baseIsRegExp(value) { return isObjectLike(value) && baseGetTag(value) == regexpTag; } /** * The base implementation of `_.isSet` without Node.js optimizations. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a set, else `false`. */ function baseIsSet(value) { return isObjectLike(value) && getTag(value) == setTag; } /** * The base implementation of `_.isTypedArray` without Node.js optimizations. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a typed array, else `false`. */ function baseIsTypedArray(value) { return isObjectLike(value) && isLength(value.length) && !!typedArrayTags[baseGetTag(value)]; } /** * The base implementation of `_.iteratee`. * * @private * @param {*} [value=_.identity] The value to convert to an iteratee. * @returns {Function} Returns the iteratee. */ function baseIteratee(value) { // Don't store the `typeof` result in a variable to avoid a JIT bug in Safari 9. // See https://bugs.webkit.org/show_bug.cgi?id=156034 for more details. if (typeof value == 'function') { return value; } if (value == null) { return identity; } if (typeof value == 'object') { return isArray(value) ? baseMatchesProperty(value[0], value[1]) : baseMatches(value); } return property(value); } /** * The base implementation of `_.keys` which doesn't treat sparse arrays as dense. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of property names. */ function baseKeys(object) { if (!isPrototype(object)) { return nativeKeys(object); } var result = []; for (var key in Object(object)) { if (hasOwnProperty.call(object, key) && key != 'constructor') { result.push(key); } } return result; } /** * The base implementation of `_.keysIn` which doesn't treat sparse arrays as dense. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of property names. */ function baseKeysIn(object) { if (!isObject(object)) { return nativeKeysIn(object); } var isProto = isPrototype(object), result = []; for (var key in object) { if (!(key == 'constructor' && (isProto || !hasOwnProperty.call(object, key)))) { result.push(key); } } return result; } /** * The base implementation of `_.lt` which doesn't coerce arguments. * * @private * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if `value` is less than `other`, * else `false`. */ function baseLt(value, other) { return value < other; } /** * The base implementation of `_.map` without support for iteratee shorthands. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} iteratee The function invoked per iteration. * @returns {Array} Returns the new mapped array. */ function baseMap(collection, iteratee) { var index = -1, result = isArrayLike(collection) ? Array(collection.length) : []; baseEach(collection, function(value, key, collection) { result[++index] = iteratee(value, key, collection); }); return result; } /** * The base implementation of `_.matches` which doesn't clone `source`. * * @private * @param {Object} source The object of property values to match. * @returns {Function} Returns the new spec function. */ function baseMatches(source) { var matchData = getMatchData(source); if (matchData.length == 1 && matchData[0][2]) { return matchesStrictComparable(matchData[0][0], matchData[0][1]); } return function(object) { return object === source || baseIsMatch(object, source, matchData); }; } /** * The base implementation of `_.matchesProperty` which doesn't clone `srcValue`. * * @private * @param {string} path The path of the property to get. * @param {*} srcValue The value to match. * @returns {Function} Returns the new spec function. */ function baseMatchesProperty(path, srcValue) { if (isKey(path) && isStrictComparable(srcValue)) { return matchesStrictComparable(toKey(path), srcValue); } return function(object) { var objValue = get(object, path); return (objValue === undefined && objValue === srcValue) ? hasIn(object, path) : baseIsEqual(srcValue, objValue, COMPARE_PARTIAL_FLAG | COMPARE_UNORDERED_FLAG); }; } /** * The base implementation of `_.merge` without support for multiple sources. * * @private * @param {Object} object The destination object. * @param {Object} source The source object. * @param {number} srcIndex The index of `source`. * @param {Function} [customizer] The function to customize merged values. * @param {Object} [stack] Tracks traversed source values and their merged * counterparts. */ function baseMerge(object, source, srcIndex, customizer, stack) { if (object === source) { return; } baseFor(source, function(srcValue, key) { if (isObject(srcValue)) { stack || (stack = new Stack); baseMergeDeep(object, source, key, srcIndex, baseMerge, customizer, stack); } else { var newValue = customizer ? customizer(object[key], srcValue, (key + ''), object, source, stack) : undefined; if (newValue === undefined) { newValue = srcValue; } assignMergeValue(object, key, newValue); } }, keysIn); } /** * A specialized version of `baseMerge` for arrays and objects which performs * deep merges and tracks traversed objects enabling objects with circular * references to be merged. * * @private * @param {Object} object The destination object. * @param {Object} source The source object. * @param {string} key The key of the value to merge. * @param {number} srcIndex The index of `source`. * @param {Function} mergeFunc The function to merge values. * @param {Function} [customizer] The function to customize assigned values. * @param {Object} [stack] Tracks traversed source values and their merged * counterparts. */ function baseMergeDeep(object, source, key, srcIndex, mergeFunc, customizer, stack) { var objValue = object[key], srcValue = source[key], stacked = stack.get(srcValue); if (stacked) { assignMergeValue(object, key, stacked); return; } var newValue = customizer ? customizer(objValue, srcValue, (key + ''), object, source, stack) : undefined; var isCommon = newValue === undefined; if (isCommon) { var isArr = isArray(srcValue), isBuff = !isArr && isBuffer(srcValue), isTyped = !isArr && !isBuff && isTypedArray(srcValue); newValue = srcValue; if (isArr || isBuff || isTyped) { if (isArray(objValue)) { newValue = objValue; } else if (isArrayLikeObject(objValue)) { newValue = copyArray(objValue); } else if (isBuff) { isCommon = false; newValue = cloneBuffer(srcValue, true); } else if (isTyped) { isCommon = false; newValue = cloneTypedArray(srcValue, true); } else { newValue = []; } } else if (isPlainObject(srcValue) || isArguments(srcValue)) { newValue = objValue; if (isArguments(objValue)) { newValue = toPlainObject(objValue); } else if (!isObject(objValue) || (srcIndex && isFunction(objValue))) { newValue = initCloneObject(srcValue); } } else { isCommon = false; } } if (isCommon) { // Recursively merge objects and arrays (susceptible to call stack limits). stack.set(srcValue, newValue); mergeFunc(newValue, srcValue, srcIndex, customizer, stack); stack['delete'](srcValue); } assignMergeValue(object, key, newValue); } /** * The base implementation of `_.nth` which doesn't coerce arguments. * * @private * @param {Array} array The array to query. * @param {number} n The index of the element to return. * @returns {*} Returns the nth element of `array`. */ function baseNth(array, n) { var length = array.length; if (!length) { return; } n += n < 0 ? length : 0; return isIndex(n, length) ? array[n] : undefined; } /** * The base implementation of `_.orderBy` without param guards. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function[]|Object[]|string[]} iteratees The iteratees to sort by. * @param {string[]} orders The sort orders of `iteratees`. * @returns {Array} Returns the new sorted array. */ function baseOrderBy(collection, iteratees, orders) { var index = -1; iteratees = arrayMap(iteratees.length ? iteratees : [identity], baseUnary(getIteratee())); var result = baseMap(collection, function(value, key, collection) { var criteria = arrayMap(iteratees, function(iteratee) { return iteratee(value); }); return { 'criteria': criteria, 'index': ++index, 'value': value }; }); return baseSortBy(result, function(object, other) { return compareMultiple(object, other, orders); }); } /** * The base implementation of `_.pick` without support for individual * property identifiers. * * @private * @param {Object} object The source object. * @param {string[]} paths The property paths to pick. * @returns {Object} Returns the new object. */ function basePick(object, paths) { return basePickBy(object, paths, function(value, path) { return hasIn(object, path); }); } /** * The base implementation of `_.pickBy` without support for iteratee shorthands. * * @private * @param {Object} object The source object. * @param {string[]} paths The property paths to pick. * @param {Function} predicate The function invoked per property. * @returns {Object} Returns the new object. */ function basePickBy(object, paths, predicate) { var index = -1, length = paths.length, result = {}; while (++index < length) { var path = paths[index], value = baseGet(object, path); if (predicate(value, path)) { baseSet(result, castPath(path, object), value); } } return result; } /** * A specialized version of `baseProperty` which supports deep paths. * * @private * @param {Array|string} path The path of the property to get. * @returns {Function} Returns the new accessor function. */ function basePropertyDeep(path) { return function(object) { return baseGet(object, path); }; } /** * The base implementation of `_.pullAllBy` without support for iteratee * shorthands. * * @private * @param {Array} array The array to modify. * @param {Array} values The values to remove. * @param {Function} [iteratee] The iteratee invoked per element. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns `array`. */ function basePullAll(array, values, iteratee, comparator) { var indexOf = comparator ? baseIndexOfWith : baseIndexOf, index = -1, length = values.length, seen = array; if (array === values) { values = copyArray(values); } if (iteratee) { seen = arrayMap(array, baseUnary(iteratee)); } while (++index < length) { var fromIndex = 0, value = values[index], computed = iteratee ? iteratee(value) : value; while ((fromIndex = indexOf(seen, computed, fromIndex, comparator)) > -1) { if (seen !== array) { splice.call(seen, fromIndex, 1); } splice.call(array, fromIndex, 1); } } return array; } /** * The base implementation of `_.pullAt` without support for individual * indexes or capturing the removed elements. * * @private * @param {Array} array The array to modify. * @param {number[]} indexes The indexes of elements to remove. * @returns {Array} Returns `array`. */ function basePullAt(array, indexes) { var length = array ? indexes.length : 0, lastIndex = length - 1; while (length--) { var index = indexes[length]; if (length == lastIndex || index !== previous) { var previous = index; if (isIndex(index)) { splice.call(array, index, 1); } else { baseUnset(array, index); } } } return array; } /** * The base implementation of `_.random` without support for returning * floating-point numbers. * * @private * @param {number} lower The lower bound. * @param {number} upper The upper bound. * @returns {number} Returns the random number. */ function baseRandom(lower, upper) { return lower + nativeFloor(nativeRandom() * (upper - lower + 1)); } /** * The base implementation of `_.range` and `_.rangeRight` which doesn't * coerce arguments. * * @private * @param {number} start The start of the range. * @param {number} end The end of the range. * @param {number} step The value to increment or decrement by. * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {Array} Returns the range of numbers. */ function baseRange(start, end, step, fromRight) { var index = -1, length = nativeMax(nativeCeil((end - start) / (step || 1)), 0), result = Array(length); while (length--) { result[fromRight ? length : ++index] = start; start += step; } return result; } /** * The base implementation of `_.repeat` which doesn't coerce arguments. * * @private * @param {string} string The string to repeat. * @param {number} n The number of times to repeat the string. * @returns {string} Returns the repeated string. */ function baseRepeat(string, n) { var result = ''; if (!string || n < 1 || n > MAX_SAFE_INTEGER) { return result; } // Leverage the exponentiation by squaring algorithm for a faster repeat. // See https://en.wikipedia.org/wiki/Exponentiation_by_squaring for more details. do { if (n % 2) { result += string; } n = nativeFloor(n / 2); if (n) { string += string; } } while (n); return result; } /** * The base implementation of `_.rest` which doesn't validate or coerce arguments. * * @private * @param {Function} func The function to apply a rest parameter to. * @param {number} [start=func.length-1] The start position of the rest parameter. * @returns {Function} Returns the new function. */ function baseRest(func, start) { return setToString(overRest(func, start, identity), func + ''); } /** * The base implementation of `_.sample`. * * @private * @param {Array|Object} collection The collection to sample. * @returns {*} Returns the random element. */ function baseSample(collection) { return arraySample(values(collection)); } /** * The base implementation of `_.sampleSize` without param guards. * * @private * @param {Array|Object} collection The collection to sample. * @param {number} n The number of elements to sample. * @returns {Array} Returns the random elements. */ function baseSampleSize(collection, n) { var array = values(collection); return shuffleSelf(array, baseClamp(n, 0, array.length)); } /** * The base implementation of `_.set`. * * @private * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to set. * @param {*} value The value to set. * @param {Function} [customizer] The function to customize path creation. * @returns {Object} Returns `object`. */ function baseSet(object, path, value, customizer) { if (!isObject(object)) { return object; } path = castPath(path, object); var index = -1, length = path.length, lastIndex = length - 1, nested = object; while (nested != null && ++index < length) { var key = toKey(path[index]), newValue = value; if (index != lastIndex) { var objValue = nested[key]; newValue = customizer ? customizer(objValue, key, nested) : undefined; if (newValue === undefined) { newValue = isObject(objValue) ? objValue : (isIndex(path[index + 1]) ? [] : {}); } } assignValue(nested, key, newValue); nested = nested[key]; } return object; } /** * The base implementation of `setData` without support for hot loop shorting. * * @private * @param {Function} func The function to associate metadata with. * @param {*} data The metadata. * @returns {Function} Returns `func`. */ var baseSetData = !metaMap ? identity : function(func, data) { metaMap.set(func, data); return func; }; /** * The base implementation of `setToString` without support for hot loop shorting. * * @private * @param {Function} func The function to modify. * @param {Function} string The `toString` result. * @returns {Function} Returns `func`. */ var baseSetToString = !defineProperty ? identity : function(func, string) { return defineProperty(func, 'toString', { 'configurable': true, 'enumerable': false, 'value': constant(string), 'writable': true }); }; /** * The base implementation of `_.shuffle`. * * @private * @param {Array|Object} collection The collection to shuffle. * @returns {Array} Returns the new shuffled array. */ function baseShuffle(collection) { return shuffleSelf(values(collection)); } /** * The base implementation of `_.slice` without an iteratee call guard. * * @private * @param {Array} array The array to slice. * @param {number} [start=0] The start position. * @param {number} [end=array.length] The end position. * @returns {Array} Returns the slice of `array`. */ function baseSlice(array, start, end) { var index = -1, length = array.length; if (start < 0) { start = -start > length ? 0 : (length + start); } end = end > length ? length : end; if (end < 0) { end += length; } length = start > end ? 0 : ((end - start) >>> 0); start >>>= 0; var result = Array(length); while (++index < length) { result[index] = array[index + start]; } return result; } /** * The base implementation of `_.some` without support for iteratee shorthands. * * @private * @param {Array|Object} collection The collection to iterate over. * @param {Function} predicate The function invoked per iteration. * @returns {boolean} Returns `true` if any element passes the predicate check, * else `false`. */ function baseSome(collection, predicate) { var result; baseEach(collection, function(value, index, collection) { result = predicate(value, index, collection); return !result; }); return !!result; } /** * The base implementation of `_.sortedIndex` and `_.sortedLastIndex` which * performs a binary search of `array` to determine the index at which `value` * should be inserted into `array` in order to maintain its sort order. * * @private * @param {Array} array The sorted array to inspect. * @param {*} value The value to evaluate. * @param {boolean} [retHighest] Specify returning the highest qualified index. * @returns {number} Returns the index at which `value` should be inserted * into `array`. */ function baseSortedIndex(array, value, retHighest) { var low = 0, high = array == null ? low : array.length; if (typeof value == 'number' && value === value && high <= HALF_MAX_ARRAY_LENGTH) { while (low < high) { var mid = (low + high) >>> 1, computed = array[mid]; if (computed !== null && !isSymbol(computed) && (retHighest ? (computed <= value) : (computed < value))) { low = mid + 1; } else { high = mid; } } return high; } return baseSortedIndexBy(array, value, identity, retHighest); } /** * The base implementation of `_.sortedIndexBy` and `_.sortedLastIndexBy` * which invokes `iteratee` for `value` and each element of `array` to compute * their sort ranking. The iteratee is invoked with one argument; (value). * * @private * @param {Array} array The sorted array to inspect. * @param {*} value The value to evaluate. * @param {Function} iteratee The iteratee invoked per element. * @param {boolean} [retHighest] Specify returning the highest qualified index. * @returns {number} Returns the index at which `value` should be inserted * into `array`. */ function baseSortedIndexBy(array, value, iteratee, retHighest) { value = iteratee(value); var low = 0, high = array == null ? 0 : array.length, valIsNaN = value !== value, valIsNull = value === null, valIsSymbol = isSymbol(value), valIsUndefined = value === undefined; while (low < high) { var mid = nativeFloor((low + high) / 2), computed = iteratee(array[mid]), othIsDefined = computed !== undefined, othIsNull = computed === null, othIsReflexive = computed === computed, othIsSymbol = isSymbol(computed); if (valIsNaN) { var setLow = retHighest || othIsReflexive; } else if (valIsUndefined) { setLow = othIsReflexive && (retHighest || othIsDefined); } else if (valIsNull) { setLow = othIsReflexive && othIsDefined && (retHighest || !othIsNull); } else if (valIsSymbol) { setLow = othIsReflexive && othIsDefined && !othIsNull && (retHighest || !othIsSymbol); } else if (othIsNull || othIsSymbol) { setLow = false; } else { setLow = retHighest ? (computed <= value) : (computed < value); } if (setLow) { low = mid + 1; } else { high = mid; } } return nativeMin(high, MAX_ARRAY_INDEX); } /** * The base implementation of `_.sortedUniq` and `_.sortedUniqBy` without * support for iteratee shorthands. * * @private * @param {Array} array The array to inspect. * @param {Function} [iteratee] The iteratee invoked per element. * @returns {Array} Returns the new duplicate free array. */ function baseSortedUniq(array, iteratee) { var index = -1, length = array.length, resIndex = 0, result = []; while (++index < length) { var value = array[index], computed = iteratee ? iteratee(value) : value; if (!index || !eq(computed, seen)) { var seen = computed; result[resIndex++] = value === 0 ? 0 : value; } } return result; } /** * The base implementation of `_.toNumber` which doesn't ensure correct * conversions of binary, hexadecimal, or octal string values. * * @private * @param {*} value The value to process. * @returns {number} Returns the number. */ function baseToNumber(value) { if (typeof value == 'number') { return value; } if (isSymbol(value)) { return NAN; } return +value; } /** * The base implementation of `_.toString` which doesn't convert nullish * values to empty strings. * * @private * @param {*} value The value to process. * @returns {string} Returns the string. */ function baseToString(value) { // Exit early for strings to avoid a performance hit in some environments. if (typeof value == 'string') { return value; } if (isArray(value)) { // Recursively convert values (susceptible to call stack limits). return arrayMap(value, baseToString) + ''; } if (isSymbol(value)) { return symbolToString ? symbolToString.call(value) : ''; } var result = (value + ''); return (result == '0' && (1 / value) == -INFINITY) ? '-0' : result; } /** * The base implementation of `_.uniqBy` without support for iteratee shorthands. * * @private * @param {Array} array The array to inspect. * @param {Function} [iteratee] The iteratee invoked per element. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new duplicate free array. */ function baseUniq(array, iteratee, comparator) { var index = -1, includes = arrayIncludes, length = array.length, isCommon = true, result = [], seen = result; if (comparator) { isCommon = false; includes = arrayIncludesWith; } else if (length >= LARGE_ARRAY_SIZE) { var set = iteratee ? null : createSet(array); if (set) { return setToArray(set); } isCommon = false; includes = cacheHas; seen = new SetCache; } else { seen = iteratee ? [] : result; } outer: while (++index < length) { var value = array[index], computed = iteratee ? iteratee(value) : value; value = (comparator || value !== 0) ? value : 0; if (isCommon && computed === computed) { var seenIndex = seen.length; while (seenIndex--) { if (seen[seenIndex] === computed) { continue outer; } } if (iteratee) { seen.push(computed); } result.push(value); } else if (!includes(seen, computed, comparator)) { if (seen !== result) { seen.push(computed); } result.push(value); } } return result; } /** * The base implementation of `_.unset`. * * @private * @param {Object} object The object to modify. * @param {Array|string} path The property path to unset. * @returns {boolean} Returns `true` if the property is deleted, else `false`. */ function baseUnset(object, path) { path = castPath(path, object); object = parent(object, path); return object == null || delete object[toKey(last(path))]; } /** * The base implementation of `_.update`. * * @private * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to update. * @param {Function} updater The function to produce the updated value. * @param {Function} [customizer] The function to customize path creation. * @returns {Object} Returns `object`. */ function baseUpdate(object, path, updater, customizer) { return baseSet(object, path, updater(baseGet(object, path)), customizer); } /** * The base implementation of methods like `_.dropWhile` and `_.takeWhile` * without support for iteratee shorthands. * * @private * @param {Array} array The array to query. * @param {Function} predicate The function invoked per iteration. * @param {boolean} [isDrop] Specify dropping elements instead of taking them. * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {Array} Returns the slice of `array`. */ function baseWhile(array, predicate, isDrop, fromRight) { var length = array.length, index = fromRight ? length : -1; while ((fromRight ? index-- : ++index < length) && predicate(array[index], index, array)) {} return isDrop ? baseSlice(array, (fromRight ? 0 : index), (fromRight ? index + 1 : length)) : baseSlice(array, (fromRight ? index + 1 : 0), (fromRight ? length : index)); } /** * The base implementation of `wrapperValue` which returns the result of * performing a sequence of actions on the unwrapped `value`, where each * successive action is supplied the return value of the previous. * * @private * @param {*} value The unwrapped value. * @param {Array} actions Actions to perform to resolve the unwrapped value. * @returns {*} Returns the resolved value. */ function baseWrapperValue(value, actions) { var result = value; if (result instanceof LazyWrapper) { result = result.value(); } return arrayReduce(actions, function(result, action) { return action.func.apply(action.thisArg, arrayPush([result], action.args)); }, result); } /** * The base implementation of methods like `_.xor`, without support for * iteratee shorthands, that accepts an array of arrays to inspect. * * @private * @param {Array} arrays The arrays to inspect. * @param {Function} [iteratee] The iteratee invoked per element. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of values. */ function baseXor(arrays, iteratee, comparator) { var length = arrays.length; if (length < 2) { return length ? baseUniq(arrays[0]) : []; } var index = -1, result = Array(length); while (++index < length) { var array = arrays[index], othIndex = -1; while (++othIndex < length) { if (othIndex != index) { result[index] = baseDifference(result[index] || array, arrays[othIndex], iteratee, comparator); } } } return baseUniq(baseFlatten(result, 1), iteratee, comparator); } /** * This base implementation of `_.zipObject` which assigns values using `assignFunc`. * * @private * @param {Array} props The property identifiers. * @param {Array} values The property values. * @param {Function} assignFunc The function to assign values. * @returns {Object} Returns the new object. */ function baseZipObject(props, values, assignFunc) { var index = -1, length = props.length, valsLength = values.length, result = {}; while (++index < length) { var value = index < valsLength ? values[index] : undefined; assignFunc(result, props[index], value); } return result; } /** * Casts `value` to an empty array if it's not an array like object. * * @private * @param {*} value The value to inspect. * @returns {Array|Object} Returns the cast array-like object. */ function castArrayLikeObject(value) { return isArrayLikeObject(value) ? value : []; } /** * Casts `value` to `identity` if it's not a function. * * @private * @param {*} value The value to inspect. * @returns {Function} Returns cast function. */ function castFunction(value) { return typeof value == 'function' ? value : identity; } /** * Casts `value` to a path array if it's not one. * * @private * @param {*} value The value to inspect. * @param {Object} [object] The object to query keys on. * @returns {Array} Returns the cast property path array. */ function castPath(value, object) { if (isArray(value)) { return value; } return isKey(value, object) ? [value] : stringToPath(toString(value)); } /** * A `baseRest` alias which can be replaced with `identity` by module * replacement plugins. * * @private * @type {Function} * @param {Function} func The function to apply a rest parameter to. * @returns {Function} Returns the new function. */ var castRest = baseRest; /** * Casts `array` to a slice if it's needed. * * @private * @param {Array} array The array to inspect. * @param {number} start The start position. * @param {number} [end=array.length] The end position. * @returns {Array} Returns the cast slice. */ function castSlice(array, start, end) { var length = array.length; end = end === undefined ? length : end; return (!start && end >= length) ? array : baseSlice(array, start, end); } /** * A simple wrapper around the global [`clearTimeout`](https://mdn.io/clearTimeout). * * @private * @param {number|Object} id The timer id or timeout object of the timer to clear. */ var clearTimeout = ctxClearTimeout || function(id) { return root.clearTimeout(id); }; /** * Creates a clone of `buffer`. * * @private * @param {Buffer} buffer The buffer to clone. * @param {boolean} [isDeep] Specify a deep clone. * @returns {Buffer} Returns the cloned buffer. */ function cloneBuffer(buffer, isDeep) { if (isDeep) { return buffer.slice(); } var length = buffer.length, result = allocUnsafe ? allocUnsafe(length) : new buffer.constructor(length); buffer.copy(result); return result; } /** * Creates a clone of `arrayBuffer`. * * @private * @param {ArrayBuffer} arrayBuffer The array buffer to clone. * @returns {ArrayBuffer} Returns the cloned array buffer. */ function cloneArrayBuffer(arrayBuffer) { var result = new arrayBuffer.constructor(arrayBuffer.byteLength); new Uint8Array(result).set(new Uint8Array(arrayBuffer)); return result; } /** * Creates a clone of `dataView`. * * @private * @param {Object} dataView The data view to clone. * @param {boolean} [isDeep] Specify a deep clone. * @returns {Object} Returns the cloned data view. */ function cloneDataView(dataView, isDeep) { var buffer = isDeep ? cloneArrayBuffer(dataView.buffer) : dataView.buffer; return new dataView.constructor(buffer, dataView.byteOffset, dataView.byteLength); } /** * Creates a clone of `map`. * * @private * @param {Object} map The map to clone. * @param {Function} cloneFunc The function to clone values. * @param {boolean} [isDeep] Specify a deep clone. * @returns {Object} Returns the cloned map. */ function cloneMap(map, isDeep, cloneFunc) { var array = isDeep ? cloneFunc(mapToArray(map), CLONE_DEEP_FLAG) : mapToArray(map); return arrayReduce(array, addMapEntry, new map.constructor); } /** * Creates a clone of `regexp`. * * @private * @param {Object} regexp The regexp to clone. * @returns {Object} Returns the cloned regexp. */ function cloneRegExp(regexp) { var result = new regexp.constructor(regexp.source, reFlags.exec(regexp)); result.lastIndex = regexp.lastIndex; return result; } /** * Creates a clone of `set`. * * @private * @param {Object} set The set to clone. * @param {Function} cloneFunc The function to clone values. * @param {boolean} [isDeep] Specify a deep clone. * @returns {Object} Returns the cloned set. */ function cloneSet(set, isDeep, cloneFunc) { var array = isDeep ? cloneFunc(setToArray(set), CLONE_DEEP_FLAG) : setToArray(set); return arrayReduce(array, addSetEntry, new set.constructor); } /** * Creates a clone of the `symbol` object. * * @private * @param {Object} symbol The symbol object to clone. * @returns {Object} Returns the cloned symbol object. */ function cloneSymbol(symbol) { return symbolValueOf ? Object(symbolValueOf.call(symbol)) : {}; } /** * Creates a clone of `typedArray`. * * @private * @param {Object} typedArray The typed array to clone. * @param {boolean} [isDeep] Specify a deep clone. * @returns {Object} Returns the cloned typed array. */ function cloneTypedArray(typedArray, isDeep) { var buffer = isDeep ? cloneArrayBuffer(typedArray.buffer) : typedArray.buffer; return new typedArray.constructor(buffer, typedArray.byteOffset, typedArray.length); } /** * Compares values to sort them in ascending order. * * @private * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {number} Returns the sort order indicator for `value`. */ function compareAscending(value, other) { if (value !== other) { var valIsDefined = value !== undefined, valIsNull = value === null, valIsReflexive = value === value, valIsSymbol = isSymbol(value); var othIsDefined = other !== undefined, othIsNull = other === null, othIsReflexive = other === other, othIsSymbol = isSymbol(other); if ((!othIsNull && !othIsSymbol && !valIsSymbol && value > other) || (valIsSymbol && othIsDefined && othIsReflexive && !othIsNull && !othIsSymbol) || (valIsNull && othIsDefined && othIsReflexive) || (!valIsDefined && othIsReflexive) || !valIsReflexive) { return 1; } if ((!valIsNull && !valIsSymbol && !othIsSymbol && value < other) || (othIsSymbol && valIsDefined && valIsReflexive && !valIsNull && !valIsSymbol) || (othIsNull && valIsDefined && valIsReflexive) || (!othIsDefined && valIsReflexive) || !othIsReflexive) { return -1; } } return 0; } /** * Used by `_.orderBy` to compare multiple properties of a value to another * and stable sort them. * * If `orders` is unspecified, all values are sorted in ascending order. Otherwise, * specify an order of "desc" for descending or "asc" for ascending sort order * of corresponding values. * * @private * @param {Object} object The object to compare. * @param {Object} other The other object to compare. * @param {boolean[]|string[]} orders The order to sort by for each property. * @returns {number} Returns the sort order indicator for `object`. */ function compareMultiple(object, other, orders) { var index = -1, objCriteria = object.criteria, othCriteria = other.criteria, length = objCriteria.length, ordersLength = orders.length; while (++index < length) { var result = compareAscending(objCriteria[index], othCriteria[index]); if (result) { if (index >= ordersLength) { return result; } var order = orders[index]; return result * (order == 'desc' ? -1 : 1); } } // Fixes an `Array#sort` bug in the JS engine embedded in Adobe applications // that causes it, under certain circumstances, to provide the same value for // `object` and `other`. See https://github.com/jashkenas/underscore/pull/1247 // for more details. // // This also ensures a stable sort in V8 and other engines. // See https://bugs.chromium.org/p/v8/issues/detail?id=90 for more details. return object.index - other.index; } /** * Creates an array that is the composition of partially applied arguments, * placeholders, and provided arguments into a single array of arguments. * * @private * @param {Array} args The provided arguments. * @param {Array} partials The arguments to prepend to those provided. * @param {Array} holders The `partials` placeholder indexes. * @params {boolean} [isCurried] Specify composing for a curried function. * @returns {Array} Returns the new array of composed arguments. */ function composeArgs(args, partials, holders, isCurried) { var argsIndex = -1, argsLength = args.length, holdersLength = holders.length, leftIndex = -1, leftLength = partials.length, rangeLength = nativeMax(argsLength - holdersLength, 0), result = Array(leftLength + rangeLength), isUncurried = !isCurried; while (++leftIndex < leftLength) { result[leftIndex] = partials[leftIndex]; } while (++argsIndex < holdersLength) { if (isUncurried || argsIndex < argsLength) { result[holders[argsIndex]] = args[argsIndex]; } } while (rangeLength--) { result[leftIndex++] = args[argsIndex++]; } return result; } /** * This function is like `composeArgs` except that the arguments composition * is tailored for `_.partialRight`. * * @private * @param {Array} args The provided arguments. * @param {Array} partials The arguments to append to those provided. * @param {Array} holders The `partials` placeholder indexes. * @params {boolean} [isCurried] Specify composing for a curried function. * @returns {Array} Returns the new array of composed arguments. */ function composeArgsRight(args, partials, holders, isCurried) { var argsIndex = -1, argsLength = args.length, holdersIndex = -1, holdersLength = holders.length, rightIndex = -1, rightLength = partials.length, rangeLength = nativeMax(argsLength - holdersLength, 0), result = Array(rangeLength + rightLength), isUncurried = !isCurried; while (++argsIndex < rangeLength) { result[argsIndex] = args[argsIndex]; } var offset = argsIndex; while (++rightIndex < rightLength) { result[offset + rightIndex] = partials[rightIndex]; } while (++holdersIndex < holdersLength) { if (isUncurried || argsIndex < argsLength) { result[offset + holders[holdersIndex]] = args[argsIndex++]; } } return result; } /** * Copies the values of `source` to `array`. * * @private * @param {Array} source The array to copy values from. * @param {Array} [array=[]] The array to copy values to. * @returns {Array} Returns `array`. */ function copyArray(source, array) { var index = -1, length = source.length; array || (array = Array(length)); while (++index < length) { array[index] = source[index]; } return array; } /** * Copies properties of `source` to `object`. * * @private * @param {Object} source The object to copy properties from. * @param {Array} props The property identifiers to copy. * @param {Object} [object={}] The object to copy properties to. * @param {Function} [customizer] The function to customize copied values. * @returns {Object} Returns `object`. */ function copyObject(source, props, object, customizer) { var isNew = !object; object || (object = {}); var index = -1, length = props.length; while (++index < length) { var key = props[index]; var newValue = customizer ? customizer(object[key], source[key], key, object, source) : undefined; if (newValue === undefined) { newValue = source[key]; } if (isNew) { baseAssignValue(object, key, newValue); } else { assignValue(object, key, newValue); } } return object; } /** * Copies own symbols of `source` to `object`. * * @private * @param {Object} source The object to copy symbols from. * @param {Object} [object={}] The object to copy symbols to. * @returns {Object} Returns `object`. */ function copySymbols(source, object) { return copyObject(source, getSymbols(source), object); } /** * Copies own and inherited symbols of `source` to `object`. * * @private * @param {Object} source The object to copy symbols from. * @param {Object} [object={}] The object to copy symbols to. * @returns {Object} Returns `object`. */ function copySymbolsIn(source, object) { return copyObject(source, getSymbolsIn(source), object); } /** * Creates a function like `_.groupBy`. * * @private * @param {Function} setter The function to set accumulator values. * @param {Function} [initializer] The accumulator object initializer. * @returns {Function} Returns the new aggregator function. */ function createAggregator(setter, initializer) { return function(collection, iteratee) { var func = isArray(collection) ? arrayAggregator : baseAggregator, accumulator = initializer ? initializer() : {}; return func(collection, setter, getIteratee(iteratee, 2), accumulator); }; } /** * Creates a function like `_.assign`. * * @private * @param {Function} assigner The function to assign values. * @returns {Function} Returns the new assigner function. */ function createAssigner(assigner) { return baseRest(function(object, sources) { var index = -1, length = sources.length, customizer = length > 1 ? sources[length - 1] : undefined, guard = length > 2 ? sources[2] : undefined; customizer = (assigner.length > 3 && typeof customizer == 'function') ? (length--, customizer) : undefined; if (guard && isIterateeCall(sources[0], sources[1], guard)) { customizer = length < 3 ? undefined : customizer; length = 1; } object = Object(object); while (++index < length) { var source = sources[index]; if (source) { assigner(object, source, index, customizer); } } return object; }); } /** * Creates a `baseEach` or `baseEachRight` function. * * @private * @param {Function} eachFunc The function to iterate over a collection. * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {Function} Returns the new base function. */ function createBaseEach(eachFunc, fromRight) { return function(collection, iteratee) { if (collection == null) { return collection; } if (!isArrayLike(collection)) { return eachFunc(collection, iteratee); } var length = collection.length, index = fromRight ? length : -1, iterable = Object(collection); while ((fromRight ? index-- : ++index < length)) { if (iteratee(iterable[index], index, iterable) === false) { break; } } return collection; }; } /** * Creates a base function for methods like `_.forIn` and `_.forOwn`. * * @private * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {Function} Returns the new base function. */ function createBaseFor(fromRight) { return function(object, iteratee, keysFunc) { var index = -1, iterable = Object(object), props = keysFunc(object), length = props.length; while (length--) { var key = props[fromRight ? length : ++index]; if (iteratee(iterable[key], key, iterable) === false) { break; } } return object; }; } /** * Creates a function that wraps `func` to invoke it with the optional `this` * binding of `thisArg`. * * @private * @param {Function} func The function to wrap. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @param {*} [thisArg] The `this` binding of `func`. * @returns {Function} Returns the new wrapped function. */ function createBind(func, bitmask, thisArg) { var isBind = bitmask & WRAP_BIND_FLAG, Ctor = createCtor(func); function wrapper() { var fn = (this && this !== root && this instanceof wrapper) ? Ctor : func; return fn.apply(isBind ? thisArg : this, arguments); } return wrapper; } /** * Creates a function like `_.lowerFirst`. * * @private * @param {string} methodName The name of the `String` case method to use. * @returns {Function} Returns the new case function. */ function createCaseFirst(methodName) { return function(string) { string = toString(string); var strSymbols = hasUnicode(string) ? stringToArray(string) : undefined; var chr = strSymbols ? strSymbols[0] : string.charAt(0); var trailing = strSymbols ? castSlice(strSymbols, 1).join('') : string.slice(1); return chr[methodName]() + trailing; }; } /** * Creates a function like `_.camelCase`. * * @private * @param {Function} callback The function to combine each word. * @returns {Function} Returns the new compounder function. */ function createCompounder(callback) { return function(string) { return arrayReduce(words(deburr(string).replace(reApos, '')), callback, ''); }; } /** * Creates a function that produces an instance of `Ctor` regardless of * whether it was invoked as part of a `new` expression or by `call` or `apply`. * * @private * @param {Function} Ctor The constructor to wrap. * @returns {Function} Returns the new wrapped function. */ function createCtor(Ctor) { return function() { // Use a `switch` statement to work with class constructors. See // http://ecma-international.org/ecma-262/7.0/#sec-ecmascript-function-objects-call-thisargument-argumentslist // for more details. var args = arguments; switch (args.length) { case 0: return new Ctor; case 1: return new Ctor(args[0]); case 2: return new Ctor(args[0], args[1]); case 3: return new Ctor(args[0], args[1], args[2]); case 4: return new Ctor(args[0], args[1], args[2], args[3]); case 5: return new Ctor(args[0], args[1], args[2], args[3], args[4]); case 6: return new Ctor(args[0], args[1], args[2], args[3], args[4], args[5]); case 7: return new Ctor(args[0], args[1], args[2], args[3], args[4], args[5], args[6]); } var thisBinding = baseCreate(Ctor.prototype), result = Ctor.apply(thisBinding, args); // Mimic the constructor's `return` behavior. // See https://es5.github.io/#x13.2.2 for more details. return isObject(result) ? result : thisBinding; }; } /** * Creates a function that wraps `func` to enable currying. * * @private * @param {Function} func The function to wrap. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @param {number} arity The arity of `func`. * @returns {Function} Returns the new wrapped function. */ function createCurry(func, bitmask, arity) { var Ctor = createCtor(func); function wrapper() { var length = arguments.length, args = Array(length), index = length, placeholder = getHolder(wrapper); while (index--) { args[index] = arguments[index]; } var holders = (length < 3 && args[0] !== placeholder && args[length - 1] !== placeholder) ? [] : replaceHolders(args, placeholder); length -= holders.length; if (length < arity) { return createRecurry( func, bitmask, createHybrid, wrapper.placeholder, undefined, args, holders, undefined, undefined, arity - length); } var fn = (this && this !== root && this instanceof wrapper) ? Ctor : func; return apply(fn, this, args); } return wrapper; } /** * Creates a `_.find` or `_.findLast` function. * * @private * @param {Function} findIndexFunc The function to find the collection index. * @returns {Function} Returns the new find function. */ function createFind(findIndexFunc) { return function(collection, predicate, fromIndex) { var iterable = Object(collection); if (!isArrayLike(collection)) { var iteratee = getIteratee(predicate, 3); collection = keys(collection); predicate = function(key) { return iteratee(iterable[key], key, iterable); }; } var index = findIndexFunc(collection, predicate, fromIndex); return index > -1 ? iterable[iteratee ? collection[index] : index] : undefined; }; } /** * Creates a `_.flow` or `_.flowRight` function. * * @private * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {Function} Returns the new flow function. */ function createFlow(fromRight) { return flatRest(function(funcs) { var length = funcs.length, index = length, prereq = LodashWrapper.prototype.thru; if (fromRight) { funcs.reverse(); } while (index--) { var func = funcs[index]; if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } if (prereq && !wrapper && getFuncName(func) == 'wrapper') { var wrapper = new LodashWrapper([], true); } } index = wrapper ? index : length; while (++index < length) { func = funcs[index]; var funcName = getFuncName(func), data = funcName == 'wrapper' ? getData(func) : undefined; if (data && isLaziable(data[0]) && data[1] == (WRAP_ARY_FLAG | WRAP_CURRY_FLAG | WRAP_PARTIAL_FLAG | WRAP_REARG_FLAG) && !data[4].length && data[9] == 1 ) { wrapper = wrapper[getFuncName(data[0])].apply(wrapper, data[3]); } else { wrapper = (func.length == 1 && isLaziable(func)) ? wrapper[funcName]() : wrapper.thru(func); } } return function() { var args = arguments, value = args[0]; if (wrapper && args.length == 1 && isArray(value)) { return wrapper.plant(value).value(); } var index = 0, result = length ? funcs[index].apply(this, args) : value; while (++index < length) { result = funcs[index].call(this, result); } return result; }; }); } /** * Creates a function that wraps `func` to invoke it with optional `this` * binding of `thisArg`, partial application, and currying. * * @private * @param {Function|string} func The function or method name to wrap. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @param {*} [thisArg] The `this` binding of `func`. * @param {Array} [partials] The arguments to prepend to those provided to * the new function. * @param {Array} [holders] The `partials` placeholder indexes. * @param {Array} [partialsRight] The arguments to append to those provided * to the new function. * @param {Array} [holdersRight] The `partialsRight` placeholder indexes. * @param {Array} [argPos] The argument positions of the new function. * @param {number} [ary] The arity cap of `func`. * @param {number} [arity] The arity of `func`. * @returns {Function} Returns the new wrapped function. */ function createHybrid(func, bitmask, thisArg, partials, holders, partialsRight, holdersRight, argPos, ary, arity) { var isAry = bitmask & WRAP_ARY_FLAG, isBind = bitmask & WRAP_BIND_FLAG, isBindKey = bitmask & WRAP_BIND_KEY_FLAG, isCurried = bitmask & (WRAP_CURRY_FLAG | WRAP_CURRY_RIGHT_FLAG), isFlip = bitmask & WRAP_FLIP_FLAG, Ctor = isBindKey ? undefined : createCtor(func); function wrapper() { var length = arguments.length, args = Array(length), index = length; while (index--) { args[index] = arguments[index]; } if (isCurried) { var placeholder = getHolder(wrapper), holdersCount = countHolders(args, placeholder); } if (partials) { args = composeArgs(args, partials, holders, isCurried); } if (partialsRight) { args = composeArgsRight(args, partialsRight, holdersRight, isCurried); } length -= holdersCount; if (isCurried && length < arity) { var newHolders = replaceHolders(args, placeholder); return createRecurry( func, bitmask, createHybrid, wrapper.placeholder, thisArg, args, newHolders, argPos, ary, arity - length ); } var thisBinding = isBind ? thisArg : this, fn = isBindKey ? thisBinding[func] : func; length = args.length; if (argPos) { args = reorder(args, argPos); } else if (isFlip && length > 1) { args.reverse(); } if (isAry && ary < length) { args.length = ary; } if (this && this !== root && this instanceof wrapper) { fn = Ctor || createCtor(fn); } return fn.apply(thisBinding, args); } return wrapper; } /** * Creates a function like `_.invertBy`. * * @private * @param {Function} setter The function to set accumulator values. * @param {Function} toIteratee The function to resolve iteratees. * @returns {Function} Returns the new inverter function. */ function createInverter(setter, toIteratee) { return function(object, iteratee) { return baseInverter(object, setter, toIteratee(iteratee), {}); }; } /** * Creates a function that performs a mathematical operation on two values. * * @private * @param {Function} operator The function to perform the operation. * @param {number} [defaultValue] The value used for `undefined` arguments. * @returns {Function} Returns the new mathematical operation function. */ function createMathOperation(operator, defaultValue) { return function(value, other) { var result; if (value === undefined && other === undefined) { return defaultValue; } if (value !== undefined) { result = value; } if (other !== undefined) { if (result === undefined) { return other; } if (typeof value == 'string' || typeof other == 'string') { value = baseToString(value); other = baseToString(other); } else { value = baseToNumber(value); other = baseToNumber(other); } result = operator(value, other); } return result; }; } /** * Creates a function like `_.over`. * * @private * @param {Function} arrayFunc The function to iterate over iteratees. * @returns {Function} Returns the new over function. */ function createOver(arrayFunc) { return flatRest(function(iteratees) { iteratees = arrayMap(iteratees, baseUnary(getIteratee())); return baseRest(function(args) { var thisArg = this; return arrayFunc(iteratees, function(iteratee) { return apply(iteratee, thisArg, args); }); }); }); } /** * Creates the padding for `string` based on `length`. The `chars` string * is truncated if the number of characters exceeds `length`. * * @private * @param {number} length The padding length. * @param {string} [chars=' '] The string used as padding. * @returns {string} Returns the padding for `string`. */ function createPadding(length, chars) { chars = chars === undefined ? ' ' : baseToString(chars); var charsLength = chars.length; if (charsLength < 2) { return charsLength ? baseRepeat(chars, length) : chars; } var result = baseRepeat(chars, nativeCeil(length / stringSize(chars))); return hasUnicode(chars) ? castSlice(stringToArray(result), 0, length).join('') : result.slice(0, length); } /** * Creates a function that wraps `func` to invoke it with the `this` binding * of `thisArg` and `partials` prepended to the arguments it receives. * * @private * @param {Function} func The function to wrap. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @param {*} thisArg The `this` binding of `func`. * @param {Array} partials The arguments to prepend to those provided to * the new function. * @returns {Function} Returns the new wrapped function. */ function createPartial(func, bitmask, thisArg, partials) { var isBind = bitmask & WRAP_BIND_FLAG, Ctor = createCtor(func); function wrapper() { var argsIndex = -1, argsLength = arguments.length, leftIndex = -1, leftLength = partials.length, args = Array(leftLength + argsLength), fn = (this && this !== root && this instanceof wrapper) ? Ctor : func; while (++leftIndex < leftLength) { args[leftIndex] = partials[leftIndex]; } while (argsLength--) { args[leftIndex++] = arguments[++argsIndex]; } return apply(fn, isBind ? thisArg : this, args); } return wrapper; } /** * Creates a `_.range` or `_.rangeRight` function. * * @private * @param {boolean} [fromRight] Specify iterating from right to left. * @returns {Function} Returns the new range function. */ function createRange(fromRight) { return function(start, end, step) { if (step && typeof step != 'number' && isIterateeCall(start, end, step)) { end = step = undefined; } // Ensure the sign of `-0` is preserved. start = toFinite(start); if (end === undefined) { end = start; start = 0; } else { end = toFinite(end); } step = step === undefined ? (start < end ? 1 : -1) : toFinite(step); return baseRange(start, end, step, fromRight); }; } /** * Creates a function that performs a relational operation on two values. * * @private * @param {Function} operator The function to perform the operation. * @returns {Function} Returns the new relational operation function. */ function createRelationalOperation(operator) { return function(value, other) { if (!(typeof value == 'string' && typeof other == 'string')) { value = toNumber(value); other = toNumber(other); } return operator(value, other); }; } /** * Creates a function that wraps `func` to continue currying. * * @private * @param {Function} func The function to wrap. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @param {Function} wrapFunc The function to create the `func` wrapper. * @param {*} placeholder The placeholder value. * @param {*} [thisArg] The `this` binding of `func`. * @param {Array} [partials] The arguments to prepend to those provided to * the new function. * @param {Array} [holders] The `partials` placeholder indexes. * @param {Array} [argPos] The argument positions of the new function. * @param {number} [ary] The arity cap of `func`. * @param {number} [arity] The arity of `func`. * @returns {Function} Returns the new wrapped function. */ function createRecurry(func, bitmask, wrapFunc, placeholder, thisArg, partials, holders, argPos, ary, arity) { var isCurry = bitmask & WRAP_CURRY_FLAG, newHolders = isCurry ? holders : undefined, newHoldersRight = isCurry ? undefined : holders, newPartials = isCurry ? partials : undefined, newPartialsRight = isCurry ? undefined : partials; bitmask |= (isCurry ? WRAP_PARTIAL_FLAG : WRAP_PARTIAL_RIGHT_FLAG); bitmask &= ~(isCurry ? WRAP_PARTIAL_RIGHT_FLAG : WRAP_PARTIAL_FLAG); if (!(bitmask & WRAP_CURRY_BOUND_FLAG)) { bitmask &= ~(WRAP_BIND_FLAG | WRAP_BIND_KEY_FLAG); } var newData = [ func, bitmask, thisArg, newPartials, newHolders, newPartialsRight, newHoldersRight, argPos, ary, arity ]; var result = wrapFunc.apply(undefined, newData); if (isLaziable(func)) { setData(result, newData); } result.placeholder = placeholder; return setWrapToString(result, func, bitmask); } /** * Creates a function like `_.round`. * * @private * @param {string} methodName The name of the `Math` method to use when rounding. * @returns {Function} Returns the new round function. */ function createRound(methodName) { var func = Math[methodName]; return function(number, precision) { number = toNumber(number); precision = precision == null ? 0 : nativeMin(toInteger(precision), 292); if (precision) { // Shift with exponential notation to avoid floating-point issues. // See [MDN](https://mdn.io/round#Examples) for more details. var pair = (toString(number) + 'e').split('e'), value = func(pair[0] + 'e' + (+pair[1] + precision)); pair = (toString(value) + 'e').split('e'); return +(pair[0] + 'e' + (+pair[1] - precision)); } return func(number); }; } /** * Creates a set object of `values`. * * @private * @param {Array} values The values to add to the set. * @returns {Object} Returns the new set. */ var createSet = !(Set && (1 / setToArray(new Set([,-0]))[1]) == INFINITY) ? noop : function(values) { return new Set(values); }; /** * Creates a `_.toPairs` or `_.toPairsIn` function. * * @private * @param {Function} keysFunc The function to get the keys of a given object. * @returns {Function} Returns the new pairs function. */ function createToPairs(keysFunc) { return function(object) { var tag = getTag(object); if (tag == mapTag) { return mapToArray(object); } if (tag == setTag) { return setToPairs(object); } return baseToPairs(object, keysFunc(object)); }; } /** * Creates a function that either curries or invokes `func` with optional * `this` binding and partially applied arguments. * * @private * @param {Function|string} func The function or method name to wrap. * @param {number} bitmask The bitmask flags. * 1 - `_.bind` * 2 - `_.bindKey` * 4 - `_.curry` or `_.curryRight` of a bound function * 8 - `_.curry` * 16 - `_.curryRight` * 32 - `_.partial` * 64 - `_.partialRight` * 128 - `_.rearg` * 256 - `_.ary` * 512 - `_.flip` * @param {*} [thisArg] The `this` binding of `func`. * @param {Array} [partials] The arguments to be partially applied. * @param {Array} [holders] The `partials` placeholder indexes. * @param {Array} [argPos] The argument positions of the new function. * @param {number} [ary] The arity cap of `func`. * @param {number} [arity] The arity of `func`. * @returns {Function} Returns the new wrapped function. */ function createWrap(func, bitmask, thisArg, partials, holders, argPos, ary, arity) { var isBindKey = bitmask & WRAP_BIND_KEY_FLAG; if (!isBindKey && typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } var length = partials ? partials.length : 0; if (!length) { bitmask &= ~(WRAP_PARTIAL_FLAG | WRAP_PARTIAL_RIGHT_FLAG); partials = holders = undefined; } ary = ary === undefined ? ary : nativeMax(toInteger(ary), 0); arity = arity === undefined ? arity : toInteger(arity); length -= holders ? holders.length : 0; if (bitmask & WRAP_PARTIAL_RIGHT_FLAG) { var partialsRight = partials, holdersRight = holders; partials = holders = undefined; } var data = isBindKey ? undefined : getData(func); var newData = [ func, bitmask, thisArg, partials, holders, partialsRight, holdersRight, argPos, ary, arity ]; if (data) { mergeData(newData, data); } func = newData[0]; bitmask = newData[1]; thisArg = newData[2]; partials = newData[3]; holders = newData[4]; arity = newData[9] = newData[9] === undefined ? (isBindKey ? 0 : func.length) : nativeMax(newData[9] - length, 0); if (!arity && bitmask & (WRAP_CURRY_FLAG | WRAP_CURRY_RIGHT_FLAG)) { bitmask &= ~(WRAP_CURRY_FLAG | WRAP_CURRY_RIGHT_FLAG); } if (!bitmask || bitmask == WRAP_BIND_FLAG) { var result = createBind(func, bitmask, thisArg); } else if (bitmask == WRAP_CURRY_FLAG || bitmask == WRAP_CURRY_RIGHT_FLAG) { result = createCurry(func, bitmask, arity); } else if ((bitmask == WRAP_PARTIAL_FLAG || bitmask == (WRAP_BIND_FLAG | WRAP_PARTIAL_FLAG)) && !holders.length) { result = createPartial(func, bitmask, thisArg, partials); } else { result = createHybrid.apply(undefined, newData); } var setter = data ? baseSetData : setData; return setWrapToString(setter(result, newData), func, bitmask); } /** * Used by `_.defaults` to customize its `_.assignIn` use to assign properties * of source objects to the destination object for all destination properties * that resolve to `undefined`. * * @private * @param {*} objValue The destination value. * @param {*} srcValue The source value. * @param {string} key The key of the property to assign. * @param {Object} object The parent object of `objValue`. * @returns {*} Returns the value to assign. */ function customDefaultsAssignIn(objValue, srcValue, key, object) { if (objValue === undefined || (eq(objValue, objectProto[key]) && !hasOwnProperty.call(object, key))) { return srcValue; } return objValue; } /** * Used by `_.defaultsDeep` to customize its `_.merge` use to merge source * objects into destination objects that are passed thru. * * @private * @param {*} objValue The destination value. * @param {*} srcValue The source value. * @param {string} key The key of the property to merge. * @param {Object} object The parent object of `objValue`. * @param {Object} source The parent object of `srcValue`. * @param {Object} [stack] Tracks traversed source values and their merged * counterparts. * @returns {*} Returns the value to assign. */ function customDefaultsMerge(objValue, srcValue, key, object, source, stack) { if (isObject(objValue) && isObject(srcValue)) { // Recursively merge objects and arrays (susceptible to call stack limits). stack.set(srcValue, objValue); baseMerge(objValue, srcValue, undefined, customDefaultsMerge, stack); stack['delete'](srcValue); } return objValue; } /** * Used by `_.omit` to customize its `_.cloneDeep` use to only clone plain * objects. * * @private * @param {*} value The value to inspect. * @param {string} key The key of the property to inspect. * @returns {*} Returns the uncloned value or `undefined` to defer cloning to `_.cloneDeep`. */ function customOmitClone(value) { return isPlainObject(value) ? undefined : value; } /** * A specialized version of `baseIsEqualDeep` for arrays with support for * partial deep comparisons. * * @private * @param {Array} array The array to compare. * @param {Array} other The other array to compare. * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. * @param {Function} customizer The function to customize comparisons. * @param {Function} equalFunc The function to determine equivalents of values. * @param {Object} stack Tracks traversed `array` and `other` objects. * @returns {boolean} Returns `true` if the arrays are equivalent, else `false`. */ function equalArrays(array, other, bitmask, customizer, equalFunc, stack) { var isPartial = bitmask & COMPARE_PARTIAL_FLAG, arrLength = array.length, othLength = other.length; if (arrLength != othLength && !(isPartial && othLength > arrLength)) { return false; } // Assume cyclic values are equal. var stacked = stack.get(array); if (stacked && stack.get(other)) { return stacked == other; } var index = -1, result = true, seen = (bitmask & COMPARE_UNORDERED_FLAG) ? new SetCache : undefined; stack.set(array, other); stack.set(other, array); // Ignore non-index properties. while (++index < arrLength) { var arrValue = array[index], othValue = other[index]; if (customizer) { var compared = isPartial ? customizer(othValue, arrValue, index, other, array, stack) : customizer(arrValue, othValue, index, array, other, stack); } if (compared !== undefined) { if (compared) { continue; } result = false; break; } // Recursively compare arrays (susceptible to call stack limits). if (seen) { if (!arraySome(other, function(othValue, othIndex) { if (!cacheHas(seen, othIndex) && (arrValue === othValue || equalFunc(arrValue, othValue, bitmask, customizer, stack))) { return seen.push(othIndex); } })) { result = false; break; } } else if (!( arrValue === othValue || equalFunc(arrValue, othValue, bitmask, customizer, stack) )) { result = false; break; } } stack['delete'](array); stack['delete'](other); return result; } /** * A specialized version of `baseIsEqualDeep` for comparing objects of * the same `toStringTag`. * * **Note:** This function only supports comparing values with tags of * `Boolean`, `Date`, `Error`, `Number`, `RegExp`, or `String`. * * @private * @param {Object} object The object to compare. * @param {Object} other The other object to compare. * @param {string} tag The `toStringTag` of the objects to compare. * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. * @param {Function} customizer The function to customize comparisons. * @param {Function} equalFunc The function to determine equivalents of values. * @param {Object} stack Tracks traversed `object` and `other` objects. * @returns {boolean} Returns `true` if the objects are equivalent, else `false`. */ function equalByTag(object, other, tag, bitmask, customizer, equalFunc, stack) { switch (tag) { case dataViewTag: if ((object.byteLength != other.byteLength) || (object.byteOffset != other.byteOffset)) { return false; } object = object.buffer; other = other.buffer; case arrayBufferTag: if ((object.byteLength != other.byteLength) || !equalFunc(new Uint8Array(object), new Uint8Array(other))) { return false; } return true; case boolTag: case dateTag: case numberTag: // Coerce booleans to `1` or `0` and dates to milliseconds. // Invalid dates are coerced to `NaN`. return eq(+object, +other); case errorTag: return object.name == other.name && object.message == other.message; case regexpTag: case stringTag: // Coerce regexes to strings and treat strings, primitives and objects, // as equal. See http://www.ecma-international.org/ecma-262/7.0/#sec-regexp.prototype.tostring // for more details. return object == (other + ''); case mapTag: var convert = mapToArray; case setTag: var isPartial = bitmask & COMPARE_PARTIAL_FLAG; convert || (convert = setToArray); if (object.size != other.size && !isPartial) { return false; } // Assume cyclic values are equal. var stacked = stack.get(object); if (stacked) { return stacked == other; } bitmask |= COMPARE_UNORDERED_FLAG; // Recursively compare objects (susceptible to call stack limits). stack.set(object, other); var result = equalArrays(convert(object), convert(other), bitmask, customizer, equalFunc, stack); stack['delete'](object); return result; case symbolTag: if (symbolValueOf) { return symbolValueOf.call(object) == symbolValueOf.call(other); } } return false; } /** * A specialized version of `baseIsEqualDeep` for objects with support for * partial deep comparisons. * * @private * @param {Object} object The object to compare. * @param {Object} other The other object to compare. * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. * @param {Function} customizer The function to customize comparisons. * @param {Function} equalFunc The function to determine equivalents of values. * @param {Object} stack Tracks traversed `object` and `other` objects. * @returns {boolean} Returns `true` if the objects are equivalent, else `false`. */ function equalObjects(object, other, bitmask, customizer, equalFunc, stack) { var isPartial = bitmask & COMPARE_PARTIAL_FLAG, objProps = getAllKeys(object), objLength = objProps.length, othProps = getAllKeys(other), othLength = othProps.length; if (objLength != othLength && !isPartial) { return false; } var index = objLength; while (index--) { var key = objProps[index]; if (!(isPartial ? key in other : hasOwnProperty.call(other, key))) { return false; } } // Assume cyclic values are equal. var stacked = stack.get(object); if (stacked && stack.get(other)) { return stacked == other; } var result = true; stack.set(object, other); stack.set(other, object); var skipCtor = isPartial; while (++index < objLength) { key = objProps[index]; var objValue = object[key], othValue = other[key]; if (customizer) { var compared = isPartial ? customizer(othValue, objValue, key, other, object, stack) : customizer(objValue, othValue, key, object, other, stack); } // Recursively compare objects (susceptible to call stack limits). if (!(compared === undefined ? (objValue === othValue || equalFunc(objValue, othValue, bitmask, customizer, stack)) : compared )) { result = false; break; } skipCtor || (skipCtor = key == 'constructor'); } if (result && !skipCtor) { var objCtor = object.constructor, othCtor = other.constructor; // Non `Object` object instances with different constructors are not equal. if (objCtor != othCtor && ('constructor' in object && 'constructor' in other) && !(typeof objCtor == 'function' && objCtor instanceof objCtor && typeof othCtor == 'function' && othCtor instanceof othCtor)) { result = false; } } stack['delete'](object); stack['delete'](other); return result; } /** * A specialized version of `baseRest` which flattens the rest array. * * @private * @param {Function} func The function to apply a rest parameter to. * @returns {Function} Returns the new function. */ function flatRest(func) { return setToString(overRest(func, undefined, flatten), func + ''); } /** * Creates an array of own enumerable property names and symbols of `object`. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of property names and symbols. */ function getAllKeys(object) { return baseGetAllKeys(object, keys, getSymbols); } /** * Creates an array of own and inherited enumerable property names and * symbols of `object`. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of property names and symbols. */ function getAllKeysIn(object) { return baseGetAllKeys(object, keysIn, getSymbolsIn); } /** * Gets metadata for `func`. * * @private * @param {Function} func The function to query. * @returns {*} Returns the metadata for `func`. */ var getData = !metaMap ? noop : function(func) { return metaMap.get(func); }; /** * Gets the name of `func`. * * @private * @param {Function} func The function to query. * @returns {string} Returns the function name. */ function getFuncName(func) { var result = (func.name + ''), array = realNames[result], length = hasOwnProperty.call(realNames, result) ? array.length : 0; while (length--) { var data = array[length], otherFunc = data.func; if (otherFunc == null || otherFunc == func) { return data.name; } } return result; } /** * Gets the argument placeholder value for `func`. * * @private * @param {Function} func The function to inspect. * @returns {*} Returns the placeholder value. */ function getHolder(func) { var object = hasOwnProperty.call(lodash, 'placeholder') ? lodash : func; return object.placeholder; } /** * Gets the appropriate "iteratee" function. If `_.iteratee` is customized, * this function returns the custom method, otherwise it returns `baseIteratee`. * If arguments are provided, the chosen function is invoked with them and * its result is returned. * * @private * @param {*} [value] The value to convert to an iteratee. * @param {number} [arity] The arity of the created iteratee. * @returns {Function} Returns the chosen function or its result. */ function getIteratee() { var result = lodash.iteratee || iteratee; result = result === iteratee ? baseIteratee : result; return arguments.length ? result(arguments[0], arguments[1]) : result; } /** * Gets the data for `map`. * * @private * @param {Object} map The map to query. * @param {string} key The reference key. * @returns {*} Returns the map data. */ function getMapData(map, key) { var data = map.__data__; return isKeyable(key) ? data[typeof key == 'string' ? 'string' : 'hash'] : data.map; } /** * Gets the property names, values, and compare flags of `object`. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the match data of `object`. */ function getMatchData(object) { var result = keys(object), length = result.length; while (length--) { var key = result[length], value = object[key]; result[length] = [key, value, isStrictComparable(value)]; } return result; } /** * Gets the native function at `key` of `object`. * * @private * @param {Object} object The object to query. * @param {string} key The key of the method to get. * @returns {*} Returns the function if it's native, else `undefined`. */ function getNative(object, key) { var value = getValue(object, key); return baseIsNative(value) ? value : undefined; } /** * A specialized version of `baseGetTag` which ignores `Symbol.toStringTag` values. * * @private * @param {*} value The value to query. * @returns {string} Returns the raw `toStringTag`. */ function getRawTag(value) { var isOwn = hasOwnProperty.call(value, symToStringTag), tag = value[symToStringTag]; try { value[symToStringTag] = undefined; var unmasked = true; } catch (e) {} var result = nativeObjectToString.call(value); if (unmasked) { if (isOwn) { value[symToStringTag] = tag; } else { delete value[symToStringTag]; } } return result; } /** * Creates an array of the own enumerable symbols of `object`. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of symbols. */ var getSymbols = !nativeGetSymbols ? stubArray : function(object) { if (object == null) { return []; } object = Object(object); return arrayFilter(nativeGetSymbols(object), function(symbol) { return propertyIsEnumerable.call(object, symbol); }); }; /** * Creates an array of the own and inherited enumerable symbols of `object`. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of symbols. */ var getSymbolsIn = !nativeGetSymbols ? stubArray : function(object) { var result = []; while (object) { arrayPush(result, getSymbols(object)); object = getPrototype(object); } return result; }; /** * Gets the `toStringTag` of `value`. * * @private * @param {*} value The value to query. * @returns {string} Returns the `toStringTag`. */ var getTag = baseGetTag; // Fallback for data views, maps, sets, and weak maps in IE 11 and promises in Node.js < 6. if ((DataView && getTag(new DataView(new ArrayBuffer(1))) != dataViewTag) || (Map && getTag(new Map) != mapTag) || (Promise && getTag(Promise.resolve()) != promiseTag) || (Set && getTag(new Set) != setTag) || (WeakMap && getTag(new WeakMap) != weakMapTag)) { getTag = function(value) { var result = baseGetTag(value), Ctor = result == objectTag ? value.constructor : undefined, ctorString = Ctor ? toSource(Ctor) : ''; if (ctorString) { switch (ctorString) { case dataViewCtorString: return dataViewTag; case mapCtorString: return mapTag; case promiseCtorString: return promiseTag; case setCtorString: return setTag; case weakMapCtorString: return weakMapTag; } } return result; }; } /** * Gets the view, applying any `transforms` to the `start` and `end` positions. * * @private * @param {number} start The start of the view. * @param {number} end The end of the view. * @param {Array} transforms The transformations to apply to the view. * @returns {Object} Returns an object containing the `start` and `end` * positions of the view. */ function getView(start, end, transforms) { var index = -1, length = transforms.length; while (++index < length) { var data = transforms[index], size = data.size; switch (data.type) { case 'drop': start += size; break; case 'dropRight': end -= size; break; case 'take': end = nativeMin(end, start + size); break; case 'takeRight': start = nativeMax(start, end - size); break; } } return { 'start': start, 'end': end }; } /** * Extracts wrapper details from the `source` body comment. * * @private * @param {string} source The source to inspect. * @returns {Array} Returns the wrapper details. */ function getWrapDetails(source) { var match = source.match(reWrapDetails); return match ? match[1].split(reSplitDetails) : []; } /** * Checks if `path` exists on `object`. * * @private * @param {Object} object The object to query. * @param {Array|string} path The path to check. * @param {Function} hasFunc The function to check properties. * @returns {boolean} Returns `true` if `path` exists, else `false`. */ function hasPath(object, path, hasFunc) { path = castPath(path, object); var index = -1, length = path.length, result = false; while (++index < length) { var key = toKey(path[index]); if (!(result = object != null && hasFunc(object, key))) { break; } object = object[key]; } if (result || ++index != length) { return result; } length = object == null ? 0 : object.length; return !!length && isLength(length) && isIndex(key, length) && (isArray(object) || isArguments(object)); } /** * Initializes an array clone. * * @private * @param {Array} array The array to clone. * @returns {Array} Returns the initialized clone. */ function initCloneArray(array) { var length = array.length, result = array.constructor(length); // Add properties assigned by `RegExp#exec`. if (length && typeof array[0] == 'string' && hasOwnProperty.call(array, 'index')) { result.index = array.index; result.input = array.input; } return result; } /** * Initializes an object clone. * * @private * @param {Object} object The object to clone. * @returns {Object} Returns the initialized clone. */ function initCloneObject(object) { return (typeof object.constructor == 'function' && !isPrototype(object)) ? baseCreate(getPrototype(object)) : {}; } /** * Initializes an object clone based on its `toStringTag`. * * **Note:** This function only supports cloning values with tags of * `Boolean`, `Date`, `Error`, `Number`, `RegExp`, or `String`. * * @private * @param {Object} object The object to clone. * @param {string} tag The `toStringTag` of the object to clone. * @param {Function} cloneFunc The function to clone values. * @param {boolean} [isDeep] Specify a deep clone. * @returns {Object} Returns the initialized clone. */ function initCloneByTag(object, tag, cloneFunc, isDeep) { var Ctor = object.constructor; switch (tag) { case arrayBufferTag: return cloneArrayBuffer(object); case boolTag: case dateTag: return new Ctor(+object); case dataViewTag: return cloneDataView(object, isDeep); case float32Tag: case float64Tag: case int8Tag: case int16Tag: case int32Tag: case uint8Tag: case uint8ClampedTag: case uint16Tag: case uint32Tag: return cloneTypedArray(object, isDeep); case mapTag: return cloneMap(object, isDeep, cloneFunc); case numberTag: case stringTag: return new Ctor(object); case regexpTag: return cloneRegExp(object); case setTag: return cloneSet(object, isDeep, cloneFunc); case symbolTag: return cloneSymbol(object); } } /** * Inserts wrapper `details` in a comment at the top of the `source` body. * * @private * @param {string} source The source to modify. * @returns {Array} details The details to insert. * @returns {string} Returns the modified source. */ function insertWrapDetails(source, details) { var length = details.length; if (!length) { return source; } var lastIndex = length - 1; details[lastIndex] = (length > 1 ? '& ' : '') + details[lastIndex]; details = details.join(length > 2 ? ', ' : ' '); return source.replace(reWrapComment, '{\n/* [wrapped with ' + details + '] */\n'); } /** * Checks if `value` is a flattenable `arguments` object or array. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is flattenable, else `false`. */ function isFlattenable(value) { return isArray(value) || isArguments(value) || !!(spreadableSymbol && value && value[spreadableSymbol]); } /** * Checks if `value` is a valid array-like index. * * @private * @param {*} value The value to check. * @param {number} [length=MAX_SAFE_INTEGER] The upper bounds of a valid index. * @returns {boolean} Returns `true` if `value` is a valid index, else `false`. */ function isIndex(value, length) { length = length == null ? MAX_SAFE_INTEGER : length; return !!length && (typeof value == 'number' || reIsUint.test(value)) && (value > -1 && value % 1 == 0 && value < length); } /** * Checks if the given arguments are from an iteratee call. * * @private * @param {*} value The potential iteratee value argument. * @param {*} index The potential iteratee index or key argument. * @param {*} object The potential iteratee object argument. * @returns {boolean} Returns `true` if the arguments are from an iteratee call, * else `false`. */ function isIterateeCall(value, index, object) { if (!isObject(object)) { return false; } var type = typeof index; if (type == 'number' ? (isArrayLike(object) && isIndex(index, object.length)) : (type == 'string' && index in object) ) { return eq(object[index], value); } return false; } /** * Checks if `value` is a property name and not a property path. * * @private * @param {*} value The value to check. * @param {Object} [object] The object to query keys on. * @returns {boolean} Returns `true` if `value` is a property name, else `false`. */ function isKey(value, object) { if (isArray(value)) { return false; } var type = typeof value; if (type == 'number' || type == 'symbol' || type == 'boolean' || value == null || isSymbol(value)) { return true; } return reIsPlainProp.test(value) || !reIsDeepProp.test(value) || (object != null && value in Object(object)); } /** * Checks if `value` is suitable for use as unique object key. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is suitable, else `false`. */ function isKeyable(value) { var type = typeof value; return (type == 'string' || type == 'number' || type == 'symbol' || type == 'boolean') ? (value !== '__proto__') : (value === null); } /** * Checks if `func` has a lazy counterpart. * * @private * @param {Function} func The function to check. * @returns {boolean} Returns `true` if `func` has a lazy counterpart, * else `false`. */ function isLaziable(func) { var funcName = getFuncName(func), other = lodash[funcName]; if (typeof other != 'function' || !(funcName in LazyWrapper.prototype)) { return false; } if (func === other) { return true; } var data = getData(other); return !!data && func === data[0]; } /** * Checks if `func` has its source masked. * * @private * @param {Function} func The function to check. * @returns {boolean} Returns `true` if `func` is masked, else `false`. */ function isMasked(func) { return !!maskSrcKey && (maskSrcKey in func); } /** * Checks if `func` is capable of being masked. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `func` is maskable, else `false`. */ var isMaskable = coreJsData ? isFunction : stubFalse; /** * Checks if `value` is likely a prototype object. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a prototype, else `false`. */ function isPrototype(value) { var Ctor = value && value.constructor, proto = (typeof Ctor == 'function' && Ctor.prototype) || objectProto; return value === proto; } /** * Checks if `value` is suitable for strict equality comparisons, i.e. `===`. * * @private * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` if suitable for strict * equality comparisons, else `false`. */ function isStrictComparable(value) { return value === value && !isObject(value); } /** * A specialized version of `matchesProperty` for source values suitable * for strict equality comparisons, i.e. `===`. * * @private * @param {string} key The key of the property to get. * @param {*} srcValue The value to match. * @returns {Function} Returns the new spec function. */ function matchesStrictComparable(key, srcValue) { return function(object) { if (object == null) { return false; } return object[key] === srcValue && (srcValue !== undefined || (key in Object(object))); }; } /** * A specialized version of `_.memoize` which clears the memoized function's * cache when it exceeds `MAX_MEMOIZE_SIZE`. * * @private * @param {Function} func The function to have its output memoized. * @returns {Function} Returns the new memoized function. */ function memoizeCapped(func) { var result = memoize(func, function(key) { if (cache.size === MAX_MEMOIZE_SIZE) { cache.clear(); } return key; }); var cache = result.cache; return result; } /** * Merges the function metadata of `source` into `data`. * * Merging metadata reduces the number of wrappers used to invoke a function. * This is possible because methods like `_.bind`, `_.curry`, and `_.partial` * may be applied regardless of execution order. Methods like `_.ary` and * `_.rearg` modify function arguments, making the order in which they are * executed important, preventing the merging of metadata. However, we make * an exception for a safe combined case where curried functions have `_.ary` * and or `_.rearg` applied. * * @private * @param {Array} data The destination metadata. * @param {Array} source The source metadata. * @returns {Array} Returns `data`. */ function mergeData(data, source) { var bitmask = data[1], srcBitmask = source[1], newBitmask = bitmask | srcBitmask, isCommon = newBitmask < (WRAP_BIND_FLAG | WRAP_BIND_KEY_FLAG | WRAP_ARY_FLAG); var isCombo = ((srcBitmask == WRAP_ARY_FLAG) && (bitmask == WRAP_CURRY_FLAG)) || ((srcBitmask == WRAP_ARY_FLAG) && (bitmask == WRAP_REARG_FLAG) && (data[7].length <= source[8])) || ((srcBitmask == (WRAP_ARY_FLAG | WRAP_REARG_FLAG)) && (source[7].length <= source[8]) && (bitmask == WRAP_CURRY_FLAG)); // Exit early if metadata can't be merged. if (!(isCommon || isCombo)) { return data; } // Use source `thisArg` if available. if (srcBitmask & WRAP_BIND_FLAG) { data[2] = source[2]; // Set when currying a bound function. newBitmask |= bitmask & WRAP_BIND_FLAG ? 0 : WRAP_CURRY_BOUND_FLAG; } // Compose partial arguments. var value = source[3]; if (value) { var partials = data[3]; data[3] = partials ? composeArgs(partials, value, source[4]) : value; data[4] = partials ? replaceHolders(data[3], PLACEHOLDER) : source[4]; } // Compose partial right arguments. value = source[5]; if (value) { partials = data[5]; data[5] = partials ? composeArgsRight(partials, value, source[6]) : value; data[6] = partials ? replaceHolders(data[5], PLACEHOLDER) : source[6]; } // Use source `argPos` if available. value = source[7]; if (value) { data[7] = value; } // Use source `ary` if it's smaller. if (srcBitmask & WRAP_ARY_FLAG) { data[8] = data[8] == null ? source[8] : nativeMin(data[8], source[8]); } // Use source `arity` if one is not provided. if (data[9] == null) { data[9] = source[9]; } // Use source `func` and merge bitmasks. data[0] = source[0]; data[1] = newBitmask; return data; } /** * This function is like * [`Object.keys`](http://ecma-international.org/ecma-262/7.0/#sec-object.keys) * except that it includes inherited enumerable properties. * * @private * @param {Object} object The object to query. * @returns {Array} Returns the array of property names. */ function nativeKeysIn(object) { var result = []; if (object != null) { for (var key in Object(object)) { result.push(key); } } return result; } /** * Converts `value` to a string using `Object.prototype.toString`. * * @private * @param {*} value The value to convert. * @returns {string} Returns the converted string. */ function objectToString(value) { return nativeObjectToString.call(value); } /** * A specialized version of `baseRest` which transforms the rest array. * * @private * @param {Function} func The function to apply a rest parameter to. * @param {number} [start=func.length-1] The start position of the rest parameter. * @param {Function} transform The rest array transform. * @returns {Function} Returns the new function. */ function overRest(func, start, transform) { start = nativeMax(start === undefined ? (func.length - 1) : start, 0); return function() { var args = arguments, index = -1, length = nativeMax(args.length - start, 0), array = Array(length); while (++index < length) { array[index] = args[start + index]; } index = -1; var otherArgs = Array(start + 1); while (++index < start) { otherArgs[index] = args[index]; } otherArgs[start] = transform(array); return apply(func, this, otherArgs); }; } /** * Gets the parent value at `path` of `object`. * * @private * @param {Object} object The object to query. * @param {Array} path The path to get the parent value of. * @returns {*} Returns the parent value. */ function parent(object, path) { return path.length < 2 ? object : baseGet(object, baseSlice(path, 0, -1)); } /** * Reorder `array` according to the specified indexes where the element at * the first index is assigned as the first element, the element at * the second index is assigned as the second element, and so on. * * @private * @param {Array} array The array to reorder. * @param {Array} indexes The arranged array indexes. * @returns {Array} Returns `array`. */ function reorder(array, indexes) { var arrLength = array.length, length = nativeMin(indexes.length, arrLength), oldArray = copyArray(array); while (length--) { var index = indexes[length]; array[length] = isIndex(index, arrLength) ? oldArray[index] : undefined; } return array; } /** * Sets metadata for `func`. * * **Note:** If this function becomes hot, i.e. is invoked a lot in a short * period of time, it will trip its breaker and transition to an identity * function to avoid garbage collection pauses in V8. See * [V8 issue 2070](https://bugs.chromium.org/p/v8/issues/detail?id=2070) * for more details. * * @private * @param {Function} func The function to associate metadata with. * @param {*} data The metadata. * @returns {Function} Returns `func`. */ var setData = shortOut(baseSetData); /** * A simple wrapper around the global [`setTimeout`](https://mdn.io/setTimeout). * * @private * @param {Function} func The function to delay. * @param {number} wait The number of milliseconds to delay invocation. * @returns {number|Object} Returns the timer id or timeout object. */ var setTimeout = ctxSetTimeout || function(func, wait) { return root.setTimeout(func, wait); }; /** * Sets the `toString` method of `func` to return `string`. * * @private * @param {Function} func The function to modify. * @param {Function} string The `toString` result. * @returns {Function} Returns `func`. */ var setToString = shortOut(baseSetToString); /** * Sets the `toString` method of `wrapper` to mimic the source of `reference` * with wrapper details in a comment at the top of the source body. * * @private * @param {Function} wrapper The function to modify. * @param {Function} reference The reference function. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @returns {Function} Returns `wrapper`. */ function setWrapToString(wrapper, reference, bitmask) { var source = (reference + ''); return setToString(wrapper, insertWrapDetails(source, updateWrapDetails(getWrapDetails(source), bitmask))); } /** * Creates a function that'll short out and invoke `identity` instead * of `func` when it's called `HOT_COUNT` or more times in `HOT_SPAN` * milliseconds. * * @private * @param {Function} func The function to restrict. * @returns {Function} Returns the new shortable function. */ function shortOut(func) { var count = 0, lastCalled = 0; return function() { var stamp = nativeNow(), remaining = HOT_SPAN - (stamp - lastCalled); lastCalled = stamp; if (remaining > 0) { if (++count >= HOT_COUNT) { return arguments[0]; } } else { count = 0; } return func.apply(undefined, arguments); }; } /** * A specialized version of `_.shuffle` which mutates and sets the size of `array`. * * @private * @param {Array} array The array to shuffle. * @param {number} [size=array.length] The size of `array`. * @returns {Array} Returns `array`. */ function shuffleSelf(array, size) { var index = -1, length = array.length, lastIndex = length - 1; size = size === undefined ? length : size; while (++index < size) { var rand = baseRandom(index, lastIndex), value = array[rand]; array[rand] = array[index]; array[index] = value; } array.length = size; return array; } /** * Converts `string` to a property path array. * * @private * @param {string} string The string to convert. * @returns {Array} Returns the property path array. */ var stringToPath = memoizeCapped(function(string) { var result = []; if (reLeadingDot.test(string)) { result.push(''); } string.replace(rePropName, function(match, number, quote, string) { result.push(quote ? string.replace(reEscapeChar, '$1') : (number || match)); }); return result; }); /** * Converts `value` to a string key if it's not a string or symbol. * * @private * @param {*} value The value to inspect. * @returns {string|symbol} Returns the key. */ function toKey(value) { if (typeof value == 'string' || isSymbol(value)) { return value; } var result = (value + ''); return (result == '0' && (1 / value) == -INFINITY) ? '-0' : result; } /** * Converts `func` to its source code. * * @private * @param {Function} func The function to convert. * @returns {string} Returns the source code. */ function toSource(func) { if (func != null) { try { return funcToString.call(func); } catch (e) {} try { return (func + ''); } catch (e) {} } return ''; } /** * Updates wrapper `details` based on `bitmask` flags. * * @private * @returns {Array} details The details to modify. * @param {number} bitmask The bitmask flags. See `createWrap` for more details. * @returns {Array} Returns `details`. */ function updateWrapDetails(details, bitmask) { arrayEach(wrapFlags, function(pair) { var value = '_.' + pair[0]; if ((bitmask & pair[1]) && !arrayIncludes(details, value)) { details.push(value); } }); return details.sort(); } /** * Creates a clone of `wrapper`. * * @private * @param {Object} wrapper The wrapper to clone. * @returns {Object} Returns the cloned wrapper. */ function wrapperClone(wrapper) { if (wrapper instanceof LazyWrapper) { return wrapper.clone(); } var result = new LodashWrapper(wrapper.__wrapped__, wrapper.__chain__); result.__actions__ = copyArray(wrapper.__actions__); result.__index__ = wrapper.__index__; result.__values__ = wrapper.__values__; return result; } /*------------------------------------------------------------------------*/ /** * Creates an array of elements split into groups the length of `size`. * If `array` can't be split evenly, the final chunk will be the remaining * elements. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to process. * @param {number} [size=1] The length of each chunk * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the new array of chunks. * @example * * _.chunk(['a', 'b', 'c', 'd'], 2); * // => [['a', 'b'], ['c', 'd']] * * _.chunk(['a', 'b', 'c', 'd'], 3); * // => [['a', 'b', 'c'], ['d']] */ function chunk(array, size, guard) { if ((guard ? isIterateeCall(array, size, guard) : size === undefined)) { size = 1; } else { size = nativeMax(toInteger(size), 0); } var length = array == null ? 0 : array.length; if (!length || size < 1) { return []; } var index = 0, resIndex = 0, result = Array(nativeCeil(length / size)); while (index < length) { result[resIndex++] = baseSlice(array, index, (index += size)); } return result; } /** * Creates an array with all falsey values removed. The values `false`, `null`, * `0`, `""`, `undefined`, and `NaN` are falsey. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to compact. * @returns {Array} Returns the new array of filtered values. * @example * * _.compact([0, 1, false, 2, '', 3]); * // => [1, 2, 3] */ function compact(array) { var index = -1, length = array == null ? 0 : array.length, resIndex = 0, result = []; while (++index < length) { var value = array[index]; if (value) { result[resIndex++] = value; } } return result; } /** * Creates a new array concatenating `array` with any additional arrays * and/or values. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to concatenate. * @param {...*} [values] The values to concatenate. * @returns {Array} Returns the new concatenated array. * @example * * var array = [1]; * var other = _.concat(array, 2, [3], [[4]]); * * console.log(other); * // => [1, 2, 3, [4]] * * console.log(array); * // => [1] */ function concat() { var length = arguments.length; if (!length) { return []; } var args = Array(length - 1), array = arguments[0], index = length; while (index--) { args[index - 1] = arguments[index]; } return arrayPush(isArray(array) ? copyArray(array) : [array], baseFlatten(args, 1)); } /** * Creates an array of `array` values not included in the other given arrays * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. The order and references of result values are * determined by the first array. * * **Note:** Unlike `_.pullAll`, this method returns a new array. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to inspect. * @param {...Array} [values] The values to exclude. * @returns {Array} Returns the new array of filtered values. * @see _.without, _.xor * @example * * _.difference([2, 1], [2, 3]); * // => [1] */ var difference = baseRest(function(array, values) { return isArrayLikeObject(array) ? baseDifference(array, baseFlatten(values, 1, isArrayLikeObject, true)) : []; }); /** * This method is like `_.difference` except that it accepts `iteratee` which * is invoked for each element of `array` and `values` to generate the criterion * by which they're compared. The order and references of result values are * determined by the first array. The iteratee is invoked with one argument: * (value). * * **Note:** Unlike `_.pullAllBy`, this method returns a new array. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {...Array} [values] The values to exclude. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Array} Returns the new array of filtered values. * @example * * _.differenceBy([2.1, 1.2], [2.3, 3.4], Math.floor); * // => [1.2] * * // The `_.property` iteratee shorthand. * _.differenceBy([{ 'x': 2 }, { 'x': 1 }], [{ 'x': 1 }], 'x'); * // => [{ 'x': 2 }] */ var differenceBy = baseRest(function(array, values) { var iteratee = last(values); if (isArrayLikeObject(iteratee)) { iteratee = undefined; } return isArrayLikeObject(array) ? baseDifference(array, baseFlatten(values, 1, isArrayLikeObject, true), getIteratee(iteratee, 2)) : []; }); /** * This method is like `_.difference` except that it accepts `comparator` * which is invoked to compare elements of `array` to `values`. The order and * references of result values are determined by the first array. The comparator * is invoked with two arguments: (arrVal, othVal). * * **Note:** Unlike `_.pullAllWith`, this method returns a new array. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {...Array} [values] The values to exclude. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of filtered values. * @example * * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; * * _.differenceWith(objects, [{ 'x': 1, 'y': 2 }], _.isEqual); * // => [{ 'x': 2, 'y': 1 }] */ var differenceWith = baseRest(function(array, values) { var comparator = last(values); if (isArrayLikeObject(comparator)) { comparator = undefined; } return isArrayLikeObject(array) ? baseDifference(array, baseFlatten(values, 1, isArrayLikeObject, true), undefined, comparator) : []; }); /** * Creates a slice of `array` with `n` elements dropped from the beginning. * * @static * @memberOf _ * @since 0.5.0 * @category Array * @param {Array} array The array to query. * @param {number} [n=1] The number of elements to drop. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the slice of `array`. * @example * * _.drop([1, 2, 3]); * // => [2, 3] * * _.drop([1, 2, 3], 2); * // => [3] * * _.drop([1, 2, 3], 5); * // => [] * * _.drop([1, 2, 3], 0); * // => [1, 2, 3] */ function drop(array, n, guard) { var length = array == null ? 0 : array.length; if (!length) { return []; } n = (guard || n === undefined) ? 1 : toInteger(n); return baseSlice(array, n < 0 ? 0 : n, length); } /** * Creates a slice of `array` with `n` elements dropped from the end. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to query. * @param {number} [n=1] The number of elements to drop. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the slice of `array`. * @example * * _.dropRight([1, 2, 3]); * // => [1, 2] * * _.dropRight([1, 2, 3], 2); * // => [1] * * _.dropRight([1, 2, 3], 5); * // => [] * * _.dropRight([1, 2, 3], 0); * // => [1, 2, 3] */ function dropRight(array, n, guard) { var length = array == null ? 0 : array.length; if (!length) { return []; } n = (guard || n === undefined) ? 1 : toInteger(n); n = length - n; return baseSlice(array, 0, n < 0 ? 0 : n); } /** * Creates a slice of `array` excluding elements dropped from the end. * Elements are dropped until `predicate` returns falsey. The predicate is * invoked with three arguments: (value, index, array). * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to query. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the slice of `array`. * @example * * var users = [ * { 'user': 'barney', 'active': true }, * { 'user': 'fred', 'active': false }, * { 'user': 'pebbles', 'active': false } * ]; * * _.dropRightWhile(users, function(o) { return !o.active; }); * // => objects for ['barney'] * * // The `_.matches` iteratee shorthand. * _.dropRightWhile(users, { 'user': 'pebbles', 'active': false }); * // => objects for ['barney', 'fred'] * * // The `_.matchesProperty` iteratee shorthand. * _.dropRightWhile(users, ['active', false]); * // => objects for ['barney'] * * // The `_.property` iteratee shorthand. * _.dropRightWhile(users, 'active'); * // => objects for ['barney', 'fred', 'pebbles'] */ function dropRightWhile(array, predicate) { return (array && array.length) ? baseWhile(array, getIteratee(predicate, 3), true, true) : []; } /** * Creates a slice of `array` excluding elements dropped from the beginning. * Elements are dropped until `predicate` returns falsey. The predicate is * invoked with three arguments: (value, index, array). * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to query. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the slice of `array`. * @example * * var users = [ * { 'user': 'barney', 'active': false }, * { 'user': 'fred', 'active': false }, * { 'user': 'pebbles', 'active': true } * ]; * * _.dropWhile(users, function(o) { return !o.active; }); * // => objects for ['pebbles'] * * // The `_.matches` iteratee shorthand. * _.dropWhile(users, { 'user': 'barney', 'active': false }); * // => objects for ['fred', 'pebbles'] * * // The `_.matchesProperty` iteratee shorthand. * _.dropWhile(users, ['active', false]); * // => objects for ['pebbles'] * * // The `_.property` iteratee shorthand. * _.dropWhile(users, 'active'); * // => objects for ['barney', 'fred', 'pebbles'] */ function dropWhile(array, predicate) { return (array && array.length) ? baseWhile(array, getIteratee(predicate, 3), true) : []; } /** * Fills elements of `array` with `value` from `start` up to, but not * including, `end`. * * **Note:** This method mutates `array`. * * @static * @memberOf _ * @since 3.2.0 * @category Array * @param {Array} array The array to fill. * @param {*} value The value to fill `array` with. * @param {number} [start=0] The start position. * @param {number} [end=array.length] The end position. * @returns {Array} Returns `array`. * @example * * var array = [1, 2, 3]; * * _.fill(array, 'a'); * console.log(array); * // => ['a', 'a', 'a'] * * _.fill(Array(3), 2); * // => [2, 2, 2] * * _.fill([4, 6, 8, 10], '*', 1, 3); * // => [4, '*', '*', 10] */ function fill(array, value, start, end) { var length = array == null ? 0 : array.length; if (!length) { return []; } if (start && typeof start != 'number' && isIterateeCall(array, value, start)) { start = 0; end = length; } return baseFill(array, value, start, end); } /** * This method is like `_.find` except that it returns the index of the first * element `predicate` returns truthy for instead of the element itself. * * @static * @memberOf _ * @since 1.1.0 * @category Array * @param {Array} array The array to inspect. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @param {number} [fromIndex=0] The index to search from. * @returns {number} Returns the index of the found element, else `-1`. * @example * * var users = [ * { 'user': 'barney', 'active': false }, * { 'user': 'fred', 'active': false }, * { 'user': 'pebbles', 'active': true } * ]; * * _.findIndex(users, function(o) { return o.user == 'barney'; }); * // => 0 * * // The `_.matches` iteratee shorthand. * _.findIndex(users, { 'user': 'fred', 'active': false }); * // => 1 * * // The `_.matchesProperty` iteratee shorthand. * _.findIndex(users, ['active', false]); * // => 0 * * // The `_.property` iteratee shorthand. * _.findIndex(users, 'active'); * // => 2 */ function findIndex(array, predicate, fromIndex) { var length = array == null ? 0 : array.length; if (!length) { return -1; } var index = fromIndex == null ? 0 : toInteger(fromIndex); if (index < 0) { index = nativeMax(length + index, 0); } return baseFindIndex(array, getIteratee(predicate, 3), index); } /** * This method is like `_.findIndex` except that it iterates over elements * of `collection` from right to left. * * @static * @memberOf _ * @since 2.0.0 * @category Array * @param {Array} array The array to inspect. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @param {number} [fromIndex=array.length-1] The index to search from. * @returns {number} Returns the index of the found element, else `-1`. * @example * * var users = [ * { 'user': 'barney', 'active': true }, * { 'user': 'fred', 'active': false }, * { 'user': 'pebbles', 'active': false } * ]; * * _.findLastIndex(users, function(o) { return o.user == 'pebbles'; }); * // => 2 * * // The `_.matches` iteratee shorthand. * _.findLastIndex(users, { 'user': 'barney', 'active': true }); * // => 0 * * // The `_.matchesProperty` iteratee shorthand. * _.findLastIndex(users, ['active', false]); * // => 2 * * // The `_.property` iteratee shorthand. * _.findLastIndex(users, 'active'); * // => 0 */ function findLastIndex(array, predicate, fromIndex) { var length = array == null ? 0 : array.length; if (!length) { return -1; } var index = length - 1; if (fromIndex !== undefined) { index = toInteger(fromIndex); index = fromIndex < 0 ? nativeMax(length + index, 0) : nativeMin(index, length - 1); } return baseFindIndex(array, getIteratee(predicate, 3), index, true); } /** * Flattens `array` a single level deep. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to flatten. * @returns {Array} Returns the new flattened array. * @example * * _.flatten([1, [2, [3, [4]], 5]]); * // => [1, 2, [3, [4]], 5] */ function flatten(array) { var length = array == null ? 0 : array.length; return length ? baseFlatten(array, 1) : []; } /** * Recursively flattens `array`. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to flatten. * @returns {Array} Returns the new flattened array. * @example * * _.flattenDeep([1, [2, [3, [4]], 5]]); * // => [1, 2, 3, 4, 5] */ function flattenDeep(array) { var length = array == null ? 0 : array.length; return length ? baseFlatten(array, INFINITY) : []; } /** * Recursively flatten `array` up to `depth` times. * * @static * @memberOf _ * @since 4.4.0 * @category Array * @param {Array} array The array to flatten. * @param {number} [depth=1] The maximum recursion depth. * @returns {Array} Returns the new flattened array. * @example * * var array = [1, [2, [3, [4]], 5]]; * * _.flattenDepth(array, 1); * // => [1, 2, [3, [4]], 5] * * _.flattenDepth(array, 2); * // => [1, 2, 3, [4], 5] */ function flattenDepth(array, depth) { var length = array == null ? 0 : array.length; if (!length) { return []; } depth = depth === undefined ? 1 : toInteger(depth); return baseFlatten(array, depth); } /** * The inverse of `_.toPairs`; this method returns an object composed * from key-value `pairs`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} pairs The key-value pairs. * @returns {Object} Returns the new object. * @example * * _.fromPairs([['a', 1], ['b', 2]]); * // => { 'a': 1, 'b': 2 } */ function fromPairs(pairs) { var index = -1, length = pairs == null ? 0 : pairs.length, result = {}; while (++index < length) { var pair = pairs[index]; result[pair[0]] = pair[1]; } return result; } /** * Gets the first element of `array`. * * @static * @memberOf _ * @since 0.1.0 * @alias first * @category Array * @param {Array} array The array to query. * @returns {*} Returns the first element of `array`. * @example * * _.head([1, 2, 3]); * // => 1 * * _.head([]); * // => undefined */ function head(array) { return (array && array.length) ? array[0] : undefined; } /** * Gets the index at which the first occurrence of `value` is found in `array` * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. If `fromIndex` is negative, it's used as the * offset from the end of `array`. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @param {number} [fromIndex=0] The index to search from. * @returns {number} Returns the index of the matched value, else `-1`. * @example * * _.indexOf([1, 2, 1, 2], 2); * // => 1 * * // Search from the `fromIndex`. * _.indexOf([1, 2, 1, 2], 2, 2); * // => 3 */ function indexOf(array, value, fromIndex) { var length = array == null ? 0 : array.length; if (!length) { return -1; } var index = fromIndex == null ? 0 : toInteger(fromIndex); if (index < 0) { index = nativeMax(length + index, 0); } return baseIndexOf(array, value, index); } /** * Gets all but the last element of `array`. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to query. * @returns {Array} Returns the slice of `array`. * @example * * _.initial([1, 2, 3]); * // => [1, 2] */ function initial(array) { var length = array == null ? 0 : array.length; return length ? baseSlice(array, 0, -1) : []; } /** * Creates an array of unique values that are included in all given arrays * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. The order and references of result values are * determined by the first array. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @returns {Array} Returns the new array of intersecting values. * @example * * _.intersection([2, 1], [2, 3]); * // => [2] */ var intersection = baseRest(function(arrays) { var mapped = arrayMap(arrays, castArrayLikeObject); return (mapped.length && mapped[0] === arrays[0]) ? baseIntersection(mapped) : []; }); /** * This method is like `_.intersection` except that it accepts `iteratee` * which is invoked for each element of each `arrays` to generate the criterion * by which they're compared. The order and references of result values are * determined by the first array. The iteratee is invoked with one argument: * (value). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Array} Returns the new array of intersecting values. * @example * * _.intersectionBy([2.1, 1.2], [2.3, 3.4], Math.floor); * // => [2.1] * * // The `_.property` iteratee shorthand. * _.intersectionBy([{ 'x': 1 }], [{ 'x': 2 }, { 'x': 1 }], 'x'); * // => [{ 'x': 1 }] */ var intersectionBy = baseRest(function(arrays) { var iteratee = last(arrays), mapped = arrayMap(arrays, castArrayLikeObject); if (iteratee === last(mapped)) { iteratee = undefined; } else { mapped.pop(); } return (mapped.length && mapped[0] === arrays[0]) ? baseIntersection(mapped, getIteratee(iteratee, 2)) : []; }); /** * This method is like `_.intersection` except that it accepts `comparator` * which is invoked to compare elements of `arrays`. The order and references * of result values are determined by the first array. The comparator is * invoked with two arguments: (arrVal, othVal). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of intersecting values. * @example * * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; * var others = [{ 'x': 1, 'y': 1 }, { 'x': 1, 'y': 2 }]; * * _.intersectionWith(objects, others, _.isEqual); * // => [{ 'x': 1, 'y': 2 }] */ var intersectionWith = baseRest(function(arrays) { var comparator = last(arrays), mapped = arrayMap(arrays, castArrayLikeObject); comparator = typeof comparator == 'function' ? comparator : undefined; if (comparator) { mapped.pop(); } return (mapped.length && mapped[0] === arrays[0]) ? baseIntersection(mapped, undefined, comparator) : []; }); /** * Converts all elements in `array` into a string separated by `separator`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to convert. * @param {string} [separator=','] The element separator. * @returns {string} Returns the joined string. * @example * * _.join(['a', 'b', 'c'], '~'); * // => 'a~b~c' */ function join(array, separator) { return array == null ? '' : nativeJoin.call(array, separator); } /** * Gets the last element of `array`. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to query. * @returns {*} Returns the last element of `array`. * @example * * _.last([1, 2, 3]); * // => 3 */ function last(array) { var length = array == null ? 0 : array.length; return length ? array[length - 1] : undefined; } /** * This method is like `_.indexOf` except that it iterates over elements of * `array` from right to left. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @param {number} [fromIndex=array.length-1] The index to search from. * @returns {number} Returns the index of the matched value, else `-1`. * @example * * _.lastIndexOf([1, 2, 1, 2], 2); * // => 3 * * // Search from the `fromIndex`. * _.lastIndexOf([1, 2, 1, 2], 2, 2); * // => 1 */ function lastIndexOf(array, value, fromIndex) { var length = array == null ? 0 : array.length; if (!length) { return -1; } var index = length; if (fromIndex !== undefined) { index = toInteger(fromIndex); index = index < 0 ? nativeMax(length + index, 0) : nativeMin(index, length - 1); } return value === value ? strictLastIndexOf(array, value, index) : baseFindIndex(array, baseIsNaN, index, true); } /** * Gets the element at index `n` of `array`. If `n` is negative, the nth * element from the end is returned. * * @static * @memberOf _ * @since 4.11.0 * @category Array * @param {Array} array The array to query. * @param {number} [n=0] The index of the element to return. * @returns {*} Returns the nth element of `array`. * @example * * var array = ['a', 'b', 'c', 'd']; * * _.nth(array, 1); * // => 'b' * * _.nth(array, -2); * // => 'c'; */ function nth(array, n) { return (array && array.length) ? baseNth(array, toInteger(n)) : undefined; } /** * Removes all given values from `array` using * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. * * **Note:** Unlike `_.without`, this method mutates `array`. Use `_.remove` * to remove elements from an array by predicate. * * @static * @memberOf _ * @since 2.0.0 * @category Array * @param {Array} array The array to modify. * @param {...*} [values] The values to remove. * @returns {Array} Returns `array`. * @example * * var array = ['a', 'b', 'c', 'a', 'b', 'c']; * * _.pull(array, 'a', 'c'); * console.log(array); * // => ['b', 'b'] */ var pull = baseRest(pullAll); /** * This method is like `_.pull` except that it accepts an array of values to remove. * * **Note:** Unlike `_.difference`, this method mutates `array`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to modify. * @param {Array} values The values to remove. * @returns {Array} Returns `array`. * @example * * var array = ['a', 'b', 'c', 'a', 'b', 'c']; * * _.pullAll(array, ['a', 'c']); * console.log(array); * // => ['b', 'b'] */ function pullAll(array, values) { return (array && array.length && values && values.length) ? basePullAll(array, values) : array; } /** * This method is like `_.pullAll` except that it accepts `iteratee` which is * invoked for each element of `array` and `values` to generate the criterion * by which they're compared. The iteratee is invoked with one argument: (value). * * **Note:** Unlike `_.differenceBy`, this method mutates `array`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to modify. * @param {Array} values The values to remove. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Array} Returns `array`. * @example * * var array = [{ 'x': 1 }, { 'x': 2 }, { 'x': 3 }, { 'x': 1 }]; * * _.pullAllBy(array, [{ 'x': 1 }, { 'x': 3 }], 'x'); * console.log(array); * // => [{ 'x': 2 }] */ function pullAllBy(array, values, iteratee) { return (array && array.length && values && values.length) ? basePullAll(array, values, getIteratee(iteratee, 2)) : array; } /** * This method is like `_.pullAll` except that it accepts `comparator` which * is invoked to compare elements of `array` to `values`. The comparator is * invoked with two arguments: (arrVal, othVal). * * **Note:** Unlike `_.differenceWith`, this method mutates `array`. * * @static * @memberOf _ * @since 4.6.0 * @category Array * @param {Array} array The array to modify. * @param {Array} values The values to remove. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns `array`. * @example * * var array = [{ 'x': 1, 'y': 2 }, { 'x': 3, 'y': 4 }, { 'x': 5, 'y': 6 }]; * * _.pullAllWith(array, [{ 'x': 3, 'y': 4 }], _.isEqual); * console.log(array); * // => [{ 'x': 1, 'y': 2 }, { 'x': 5, 'y': 6 }] */ function pullAllWith(array, values, comparator) { return (array && array.length && values && values.length) ? basePullAll(array, values, undefined, comparator) : array; } /** * Removes elements from `array` corresponding to `indexes` and returns an * array of removed elements. * * **Note:** Unlike `_.at`, this method mutates `array`. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to modify. * @param {...(number|number[])} [indexes] The indexes of elements to remove. * @returns {Array} Returns the new array of removed elements. * @example * * var array = ['a', 'b', 'c', 'd']; * var pulled = _.pullAt(array, [1, 3]); * * console.log(array); * // => ['a', 'c'] * * console.log(pulled); * // => ['b', 'd'] */ var pullAt = flatRest(function(array, indexes) { var length = array == null ? 0 : array.length, result = baseAt(array, indexes); basePullAt(array, arrayMap(indexes, function(index) { return isIndex(index, length) ? +index : index; }).sort(compareAscending)); return result; }); /** * Removes all elements from `array` that `predicate` returns truthy for * and returns an array of the removed elements. The predicate is invoked * with three arguments: (value, index, array). * * **Note:** Unlike `_.filter`, this method mutates `array`. Use `_.pull` * to pull elements from an array by value. * * @static * @memberOf _ * @since 2.0.0 * @category Array * @param {Array} array The array to modify. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the new array of removed elements. * @example * * var array = [1, 2, 3, 4]; * var evens = _.remove(array, function(n) { * return n % 2 == 0; * }); * * console.log(array); * // => [1, 3] * * console.log(evens); * // => [2, 4] */ function remove(array, predicate) { var result = []; if (!(array && array.length)) { return result; } var index = -1, indexes = [], length = array.length; predicate = getIteratee(predicate, 3); while (++index < length) { var value = array[index]; if (predicate(value, index, array)) { result.push(value); indexes.push(index); } } basePullAt(array, indexes); return result; } /** * Reverses `array` so that the first element becomes the last, the second * element becomes the second to last, and so on. * * **Note:** This method mutates `array` and is based on * [`Array#reverse`](https://mdn.io/Array/reverse). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to modify. * @returns {Array} Returns `array`. * @example * * var array = [1, 2, 3]; * * _.reverse(array); * // => [3, 2, 1] * * console.log(array); * // => [3, 2, 1] */ function reverse(array) { return array == null ? array : nativeReverse.call(array); } /** * Creates a slice of `array` from `start` up to, but not including, `end`. * * **Note:** This method is used instead of * [`Array#slice`](https://mdn.io/Array/slice) to ensure dense arrays are * returned. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to slice. * @param {number} [start=0] The start position. * @param {number} [end=array.length] The end position. * @returns {Array} Returns the slice of `array`. */ function slice(array, start, end) { var length = array == null ? 0 : array.length; if (!length) { return []; } if (end && typeof end != 'number' && isIterateeCall(array, start, end)) { start = 0; end = length; } else { start = start == null ? 0 : toInteger(start); end = end === undefined ? length : toInteger(end); } return baseSlice(array, start, end); } /** * Uses a binary search to determine the lowest index at which `value` * should be inserted into `array` in order to maintain its sort order. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The sorted array to inspect. * @param {*} value The value to evaluate. * @returns {number} Returns the index at which `value` should be inserted * into `array`. * @example * * _.sortedIndex([30, 50], 40); * // => 1 */ function sortedIndex(array, value) { return baseSortedIndex(array, value); } /** * This method is like `_.sortedIndex` except that it accepts `iteratee` * which is invoked for `value` and each element of `array` to compute their * sort ranking. The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The sorted array to inspect. * @param {*} value The value to evaluate. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {number} Returns the index at which `value` should be inserted * into `array`. * @example * * var objects = [{ 'x': 4 }, { 'x': 5 }]; * * _.sortedIndexBy(objects, { 'x': 4 }, function(o) { return o.x; }); * // => 0 * * // The `_.property` iteratee shorthand. * _.sortedIndexBy(objects, { 'x': 4 }, 'x'); * // => 0 */ function sortedIndexBy(array, value, iteratee) { return baseSortedIndexBy(array, value, getIteratee(iteratee, 2)); } /** * This method is like `_.indexOf` except that it performs a binary * search on a sorted `array`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @returns {number} Returns the index of the matched value, else `-1`. * @example * * _.sortedIndexOf([4, 5, 5, 5, 6], 5); * // => 1 */ function sortedIndexOf(array, value) { var length = array == null ? 0 : array.length; if (length) { var index = baseSortedIndex(array, value); if (index < length && eq(array[index], value)) { return index; } } return -1; } /** * This method is like `_.sortedIndex` except that it returns the highest * index at which `value` should be inserted into `array` in order to * maintain its sort order. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The sorted array to inspect. * @param {*} value The value to evaluate. * @returns {number} Returns the index at which `value` should be inserted * into `array`. * @example * * _.sortedLastIndex([4, 5, 5, 5, 6], 5); * // => 4 */ function sortedLastIndex(array, value) { return baseSortedIndex(array, value, true); } /** * This method is like `_.sortedLastIndex` except that it accepts `iteratee` * which is invoked for `value` and each element of `array` to compute their * sort ranking. The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The sorted array to inspect. * @param {*} value The value to evaluate. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {number} Returns the index at which `value` should be inserted * into `array`. * @example * * var objects = [{ 'x': 4 }, { 'x': 5 }]; * * _.sortedLastIndexBy(objects, { 'x': 4 }, function(o) { return o.x; }); * // => 1 * * // The `_.property` iteratee shorthand. * _.sortedLastIndexBy(objects, { 'x': 4 }, 'x'); * // => 1 */ function sortedLastIndexBy(array, value, iteratee) { return baseSortedIndexBy(array, value, getIteratee(iteratee, 2), true); } /** * This method is like `_.lastIndexOf` except that it performs a binary * search on a sorted `array`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {*} value The value to search for. * @returns {number} Returns the index of the matched value, else `-1`. * @example * * _.sortedLastIndexOf([4, 5, 5, 5, 6], 5); * // => 3 */ function sortedLastIndexOf(array, value) { var length = array == null ? 0 : array.length; if (length) { var index = baseSortedIndex(array, value, true) - 1; if (eq(array[index], value)) { return index; } } return -1; } /** * This method is like `_.uniq` except that it's designed and optimized * for sorted arrays. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @returns {Array} Returns the new duplicate free array. * @example * * _.sortedUniq([1, 1, 2]); * // => [1, 2] */ function sortedUniq(array) { return (array && array.length) ? baseSortedUniq(array) : []; } /** * This method is like `_.uniqBy` except that it's designed and optimized * for sorted arrays. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {Function} [iteratee] The iteratee invoked per element. * @returns {Array} Returns the new duplicate free array. * @example * * _.sortedUniqBy([1.1, 1.2, 2.3, 2.4], Math.floor); * // => [1.1, 2.3] */ function sortedUniqBy(array, iteratee) { return (array && array.length) ? baseSortedUniq(array, getIteratee(iteratee, 2)) : []; } /** * Gets all but the first element of `array`. * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to query. * @returns {Array} Returns the slice of `array`. * @example * * _.tail([1, 2, 3]); * // => [2, 3] */ function tail(array) { var length = array == null ? 0 : array.length; return length ? baseSlice(array, 1, length) : []; } /** * Creates a slice of `array` with `n` elements taken from the beginning. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to query. * @param {number} [n=1] The number of elements to take. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the slice of `array`. * @example * * _.take([1, 2, 3]); * // => [1] * * _.take([1, 2, 3], 2); * // => [1, 2] * * _.take([1, 2, 3], 5); * // => [1, 2, 3] * * _.take([1, 2, 3], 0); * // => [] */ function take(array, n, guard) { if (!(array && array.length)) { return []; } n = (guard || n === undefined) ? 1 : toInteger(n); return baseSlice(array, 0, n < 0 ? 0 : n); } /** * Creates a slice of `array` with `n` elements taken from the end. * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to query. * @param {number} [n=1] The number of elements to take. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the slice of `array`. * @example * * _.takeRight([1, 2, 3]); * // => [3] * * _.takeRight([1, 2, 3], 2); * // => [2, 3] * * _.takeRight([1, 2, 3], 5); * // => [1, 2, 3] * * _.takeRight([1, 2, 3], 0); * // => [] */ function takeRight(array, n, guard) { var length = array == null ? 0 : array.length; if (!length) { return []; } n = (guard || n === undefined) ? 1 : toInteger(n); n = length - n; return baseSlice(array, n < 0 ? 0 : n, length); } /** * Creates a slice of `array` with elements taken from the end. Elements are * taken until `predicate` returns falsey. The predicate is invoked with * three arguments: (value, index, array). * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to query. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the slice of `array`. * @example * * var users = [ * { 'user': 'barney', 'active': true }, * { 'user': 'fred', 'active': false }, * { 'user': 'pebbles', 'active': false } * ]; * * _.takeRightWhile(users, function(o) { return !o.active; }); * // => objects for ['fred', 'pebbles'] * * // The `_.matches` iteratee shorthand. * _.takeRightWhile(users, { 'user': 'pebbles', 'active': false }); * // => objects for ['pebbles'] * * // The `_.matchesProperty` iteratee shorthand. * _.takeRightWhile(users, ['active', false]); * // => objects for ['fred', 'pebbles'] * * // The `_.property` iteratee shorthand. * _.takeRightWhile(users, 'active'); * // => [] */ function takeRightWhile(array, predicate) { return (array && array.length) ? baseWhile(array, getIteratee(predicate, 3), false, true) : []; } /** * Creates a slice of `array` with elements taken from the beginning. Elements * are taken until `predicate` returns falsey. The predicate is invoked with * three arguments: (value, index, array). * * @static * @memberOf _ * @since 3.0.0 * @category Array * @param {Array} array The array to query. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the slice of `array`. * @example * * var users = [ * { 'user': 'barney', 'active': false }, * { 'user': 'fred', 'active': false }, * { 'user': 'pebbles', 'active': true } * ]; * * _.takeWhile(users, function(o) { return !o.active; }); * // => objects for ['barney', 'fred'] * * // The `_.matches` iteratee shorthand. * _.takeWhile(users, { 'user': 'barney', 'active': false }); * // => objects for ['barney'] * * // The `_.matchesProperty` iteratee shorthand. * _.takeWhile(users, ['active', false]); * // => objects for ['barney', 'fred'] * * // The `_.property` iteratee shorthand. * _.takeWhile(users, 'active'); * // => [] */ function takeWhile(array, predicate) { return (array && array.length) ? baseWhile(array, getIteratee(predicate, 3)) : []; } /** * Creates an array of unique values, in order, from all given arrays using * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @returns {Array} Returns the new array of combined values. * @example * * _.union([2], [1, 2]); * // => [2, 1] */ var union = baseRest(function(arrays) { return baseUniq(baseFlatten(arrays, 1, isArrayLikeObject, true)); }); /** * This method is like `_.union` except that it accepts `iteratee` which is * invoked for each element of each `arrays` to generate the criterion by * which uniqueness is computed. Result values are chosen from the first * array in which the value occurs. The iteratee is invoked with one argument: * (value). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Array} Returns the new array of combined values. * @example * * _.unionBy([2.1], [1.2, 2.3], Math.floor); * // => [2.1, 1.2] * * // The `_.property` iteratee shorthand. * _.unionBy([{ 'x': 1 }], [{ 'x': 2 }, { 'x': 1 }], 'x'); * // => [{ 'x': 1 }, { 'x': 2 }] */ var unionBy = baseRest(function(arrays) { var iteratee = last(arrays); if (isArrayLikeObject(iteratee)) { iteratee = undefined; } return baseUniq(baseFlatten(arrays, 1, isArrayLikeObject, true), getIteratee(iteratee, 2)); }); /** * This method is like `_.union` except that it accepts `comparator` which * is invoked to compare elements of `arrays`. Result values are chosen from * the first array in which the value occurs. The comparator is invoked * with two arguments: (arrVal, othVal). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of combined values. * @example * * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; * var others = [{ 'x': 1, 'y': 1 }, { 'x': 1, 'y': 2 }]; * * _.unionWith(objects, others, _.isEqual); * // => [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }, { 'x': 1, 'y': 1 }] */ var unionWith = baseRest(function(arrays) { var comparator = last(arrays); comparator = typeof comparator == 'function' ? comparator : undefined; return baseUniq(baseFlatten(arrays, 1, isArrayLikeObject, true), undefined, comparator); }); /** * Creates a duplicate-free version of an array, using * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons, in which only the first occurrence of each element * is kept. The order of result values is determined by the order they occur * in the array. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to inspect. * @returns {Array} Returns the new duplicate free array. * @example * * _.uniq([2, 1, 2]); * // => [2, 1] */ function uniq(array) { return (array && array.length) ? baseUniq(array) : []; } /** * This method is like `_.uniq` except that it accepts `iteratee` which is * invoked for each element in `array` to generate the criterion by which * uniqueness is computed. The order of result values is determined by the * order they occur in the array. The iteratee is invoked with one argument: * (value). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Array} Returns the new duplicate free array. * @example * * _.uniqBy([2.1, 1.2, 2.3], Math.floor); * // => [2.1, 1.2] * * // The `_.property` iteratee shorthand. * _.uniqBy([{ 'x': 1 }, { 'x': 2 }, { 'x': 1 }], 'x'); * // => [{ 'x': 1 }, { 'x': 2 }] */ function uniqBy(array, iteratee) { return (array && array.length) ? baseUniq(array, getIteratee(iteratee, 2)) : []; } /** * This method is like `_.uniq` except that it accepts `comparator` which * is invoked to compare elements of `array`. The order of result values is * determined by the order they occur in the array.The comparator is invoked * with two arguments: (arrVal, othVal). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {Array} array The array to inspect. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new duplicate free array. * @example * * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }, { 'x': 1, 'y': 2 }]; * * _.uniqWith(objects, _.isEqual); * // => [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }] */ function uniqWith(array, comparator) { comparator = typeof comparator == 'function' ? comparator : undefined; return (array && array.length) ? baseUniq(array, undefined, comparator) : []; } /** * This method is like `_.zip` except that it accepts an array of grouped * elements and creates an array regrouping the elements to their pre-zip * configuration. * * @static * @memberOf _ * @since 1.2.0 * @category Array * @param {Array} array The array of grouped elements to process. * @returns {Array} Returns the new array of regrouped elements. * @example * * var zipped = _.zip(['a', 'b'], [1, 2], [true, false]); * // => [['a', 1, true], ['b', 2, false]] * * _.unzip(zipped); * // => [['a', 'b'], [1, 2], [true, false]] */ function unzip(array) { if (!(array && array.length)) { return []; } var length = 0; array = arrayFilter(array, function(group) { if (isArrayLikeObject(group)) { length = nativeMax(group.length, length); return true; } }); return baseTimes(length, function(index) { return arrayMap(array, baseProperty(index)); }); } /** * This method is like `_.unzip` except that it accepts `iteratee` to specify * how regrouped values should be combined. The iteratee is invoked with the * elements of each group: (...group). * * @static * @memberOf _ * @since 3.8.0 * @category Array * @param {Array} array The array of grouped elements to process. * @param {Function} [iteratee=_.identity] The function to combine * regrouped values. * @returns {Array} Returns the new array of regrouped elements. * @example * * var zipped = _.zip([1, 2], [10, 20], [100, 200]); * // => [[1, 10, 100], [2, 20, 200]] * * _.unzipWith(zipped, _.add); * // => [3, 30, 300] */ function unzipWith(array, iteratee) { if (!(array && array.length)) { return []; } var result = unzip(array); if (iteratee == null) { return result; } return arrayMap(result, function(group) { return apply(iteratee, undefined, group); }); } /** * Creates an array excluding all given values using * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * for equality comparisons. * * **Note:** Unlike `_.pull`, this method returns a new array. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {Array} array The array to inspect. * @param {...*} [values] The values to exclude. * @returns {Array} Returns the new array of filtered values. * @see _.difference, _.xor * @example * * _.without([2, 1, 2, 3], 1, 2); * // => [3] */ var without = baseRest(function(array, values) { return isArrayLikeObject(array) ? baseDifference(array, values) : []; }); /** * Creates an array of unique values that is the * [symmetric difference](https://en.wikipedia.org/wiki/Symmetric_difference) * of the given arrays. The order of result values is determined by the order * they occur in the arrays. * * @static * @memberOf _ * @since 2.4.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @returns {Array} Returns the new array of filtered values. * @see _.difference, _.without * @example * * _.xor([2, 1], [2, 3]); * // => [1, 3] */ var xor = baseRest(function(arrays) { return baseXor(arrayFilter(arrays, isArrayLikeObject)); }); /** * This method is like `_.xor` except that it accepts `iteratee` which is * invoked for each element of each `arrays` to generate the criterion by * which by which they're compared. The order of result values is determined * by the order they occur in the arrays. The iteratee is invoked with one * argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Array} Returns the new array of filtered values. * @example * * _.xorBy([2.1, 1.2], [2.3, 3.4], Math.floor); * // => [1.2, 3.4] * * // The `_.property` iteratee shorthand. * _.xorBy([{ 'x': 1 }], [{ 'x': 2 }, { 'x': 1 }], 'x'); * // => [{ 'x': 2 }] */ var xorBy = baseRest(function(arrays) { var iteratee = last(arrays); if (isArrayLikeObject(iteratee)) { iteratee = undefined; } return baseXor(arrayFilter(arrays, isArrayLikeObject), getIteratee(iteratee, 2)); }); /** * This method is like `_.xor` except that it accepts `comparator` which is * invoked to compare elements of `arrays`. The order of result values is * determined by the order they occur in the arrays. The comparator is invoked * with two arguments: (arrVal, othVal). * * @static * @memberOf _ * @since 4.0.0 * @category Array * @param {...Array} [arrays] The arrays to inspect. * @param {Function} [comparator] The comparator invoked per element. * @returns {Array} Returns the new array of filtered values. * @example * * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; * var others = [{ 'x': 1, 'y': 1 }, { 'x': 1, 'y': 2 }]; * * _.xorWith(objects, others, _.isEqual); * // => [{ 'x': 2, 'y': 1 }, { 'x': 1, 'y': 1 }] */ var xorWith = baseRest(function(arrays) { var comparator = last(arrays); comparator = typeof comparator == 'function' ? comparator : undefined; return baseXor(arrayFilter(arrays, isArrayLikeObject), undefined, comparator); }); /** * Creates an array of grouped elements, the first of which contains the * first elements of the given arrays, the second of which contains the * second elements of the given arrays, and so on. * * @static * @memberOf _ * @since 0.1.0 * @category Array * @param {...Array} [arrays] The arrays to process. * @returns {Array} Returns the new array of grouped elements. * @example * * _.zip(['a', 'b'], [1, 2], [true, false]); * // => [['a', 1, true], ['b', 2, false]] */ var zip = baseRest(unzip); /** * This method is like `_.fromPairs` except that it accepts two arrays, * one of property identifiers and one of corresponding values. * * @static * @memberOf _ * @since 0.4.0 * @category Array * @param {Array} [props=[]] The property identifiers. * @param {Array} [values=[]] The property values. * @returns {Object} Returns the new object. * @example * * _.zipObject(['a', 'b'], [1, 2]); * // => { 'a': 1, 'b': 2 } */ function zipObject(props, values) { return baseZipObject(props || [], values || [], assignValue); } /** * This method is like `_.zipObject` except that it supports property paths. * * @static * @memberOf _ * @since 4.1.0 * @category Array * @param {Array} [props=[]] The property identifiers. * @param {Array} [values=[]] The property values. * @returns {Object} Returns the new object. * @example * * _.zipObjectDeep(['a.b[0].c', 'a.b[1].d'], [1, 2]); * // => { 'a': { 'b': [{ 'c': 1 }, { 'd': 2 }] } } */ function zipObjectDeep(props, values) { return baseZipObject(props || [], values || [], baseSet); } /** * This method is like `_.zip` except that it accepts `iteratee` to specify * how grouped values should be combined. The iteratee is invoked with the * elements of each group: (...group). * * @static * @memberOf _ * @since 3.8.0 * @category Array * @param {...Array} [arrays] The arrays to process. * @param {Function} [iteratee=_.identity] The function to combine * grouped values. * @returns {Array} Returns the new array of grouped elements. * @example * * _.zipWith([1, 2], [10, 20], [100, 200], function(a, b, c) { * return a + b + c; * }); * // => [111, 222] */ var zipWith = baseRest(function(arrays) { var length = arrays.length, iteratee = length > 1 ? arrays[length - 1] : undefined; iteratee = typeof iteratee == 'function' ? (arrays.pop(), iteratee) : undefined; return unzipWith(arrays, iteratee); }); /*------------------------------------------------------------------------*/ /** * Creates a `lodash` wrapper instance that wraps `value` with explicit method * chain sequences enabled. The result of such sequences must be unwrapped * with `_#value`. * * @static * @memberOf _ * @since 1.3.0 * @category Seq * @param {*} value The value to wrap. * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * var users = [ * { 'user': 'barney', 'age': 36 }, * { 'user': 'fred', 'age': 40 }, * { 'user': 'pebbles', 'age': 1 } * ]; * * var youngest = _ * .chain(users) * .sortBy('age') * .map(function(o) { * return o.user + ' is ' + o.age; * }) * .head() * .value(); * // => 'pebbles is 1' */ function chain(value) { var result = lodash(value); result.__chain__ = true; return result; } /** * This method invokes `interceptor` and returns `value`. The interceptor * is invoked with one argument; (value). The purpose of this method is to * "tap into" a method chain sequence in order to modify intermediate results. * * @static * @memberOf _ * @since 0.1.0 * @category Seq * @param {*} value The value to provide to `interceptor`. * @param {Function} interceptor The function to invoke. * @returns {*} Returns `value`. * @example * * _([1, 2, 3]) * .tap(function(array) { * // Mutate input array. * array.pop(); * }) * .reverse() * .value(); * // => [2, 1] */ function tap(value, interceptor) { interceptor(value); return value; } /** * This method is like `_.tap` except that it returns the result of `interceptor`. * The purpose of this method is to "pass thru" values replacing intermediate * results in a method chain sequence. * * @static * @memberOf _ * @since 3.0.0 * @category Seq * @param {*} value The value to provide to `interceptor`. * @param {Function} interceptor The function to invoke. * @returns {*} Returns the result of `interceptor`. * @example * * _(' abc ') * .chain() * .trim() * .thru(function(value) { * return [value]; * }) * .value(); * // => ['abc'] */ function thru(value, interceptor) { return interceptor(value); } /** * This method is the wrapper version of `_.at`. * * @name at * @memberOf _ * @since 1.0.0 * @category Seq * @param {...(string|string[])} [paths] The property paths to pick. * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * var object = { 'a': [{ 'b': { 'c': 3 } }, 4] }; * * _(object).at(['a[0].b.c', 'a[1]']).value(); * // => [3, 4] */ var wrapperAt = flatRest(function(paths) { var length = paths.length, start = length ? paths[0] : 0, value = this.__wrapped__, interceptor = function(object) { return baseAt(object, paths); }; if (length > 1 || this.__actions__.length || !(value instanceof LazyWrapper) || !isIndex(start)) { return this.thru(interceptor); } value = value.slice(start, +start + (length ? 1 : 0)); value.__actions__.push({ 'func': thru, 'args': [interceptor], 'thisArg': undefined }); return new LodashWrapper(value, this.__chain__).thru(function(array) { if (length && !array.length) { array.push(undefined); } return array; }); }); /** * Creates a `lodash` wrapper instance with explicit method chain sequences enabled. * * @name chain * @memberOf _ * @since 0.1.0 * @category Seq * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * var users = [ * { 'user': 'barney', 'age': 36 }, * { 'user': 'fred', 'age': 40 } * ]; * * // A sequence without explicit chaining. * _(users).head(); * // => { 'user': 'barney', 'age': 36 } * * // A sequence with explicit chaining. * _(users) * .chain() * .head() * .pick('user') * .value(); * // => { 'user': 'barney' } */ function wrapperChain() { return chain(this); } /** * Executes the chain sequence and returns the wrapped result. * * @name commit * @memberOf _ * @since 3.2.0 * @category Seq * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * var array = [1, 2]; * var wrapped = _(array).push(3); * * console.log(array); * // => [1, 2] * * wrapped = wrapped.commit(); * console.log(array); * // => [1, 2, 3] * * wrapped.last(); * // => 3 * * console.log(array); * // => [1, 2, 3] */ function wrapperCommit() { return new LodashWrapper(this.value(), this.__chain__); } /** * Gets the next value on a wrapped object following the * [iterator protocol](https://mdn.io/iteration_protocols#iterator). * * @name next * @memberOf _ * @since 4.0.0 * @category Seq * @returns {Object} Returns the next iterator value. * @example * * var wrapped = _([1, 2]); * * wrapped.next(); * // => { 'done': false, 'value': 1 } * * wrapped.next(); * // => { 'done': false, 'value': 2 } * * wrapped.next(); * // => { 'done': true, 'value': undefined } */ function wrapperNext() { if (this.__values__ === undefined) { this.__values__ = toArray(this.value()); } var done = this.__index__ >= this.__values__.length, value = done ? undefined : this.__values__[this.__index__++]; return { 'done': done, 'value': value }; } /** * Enables the wrapper to be iterable. * * @name Symbol.iterator * @memberOf _ * @since 4.0.0 * @category Seq * @returns {Object} Returns the wrapper object. * @example * * var wrapped = _([1, 2]); * * wrapped[Symbol.iterator]() === wrapped; * // => true * * Array.from(wrapped); * // => [1, 2] */ function wrapperToIterator() { return this; } /** * Creates a clone of the chain sequence planting `value` as the wrapped value. * * @name plant * @memberOf _ * @since 3.2.0 * @category Seq * @param {*} value The value to plant. * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * function square(n) { * return n * n; * } * * var wrapped = _([1, 2]).map(square); * var other = wrapped.plant([3, 4]); * * other.value(); * // => [9, 16] * * wrapped.value(); * // => [1, 4] */ function wrapperPlant(value) { var result, parent = this; while (parent instanceof baseLodash) { var clone = wrapperClone(parent); clone.__index__ = 0; clone.__values__ = undefined; if (result) { previous.__wrapped__ = clone; } else { result = clone; } var previous = clone; parent = parent.__wrapped__; } previous.__wrapped__ = value; return result; } /** * This method is the wrapper version of `_.reverse`. * * **Note:** This method mutates the wrapped array. * * @name reverse * @memberOf _ * @since 0.1.0 * @category Seq * @returns {Object} Returns the new `lodash` wrapper instance. * @example * * var array = [1, 2, 3]; * * _(array).reverse().value() * // => [3, 2, 1] * * console.log(array); * // => [3, 2, 1] */ function wrapperReverse() { var value = this.__wrapped__; if (value instanceof LazyWrapper) { var wrapped = value; if (this.__actions__.length) { wrapped = new LazyWrapper(this); } wrapped = wrapped.reverse(); wrapped.__actions__.push({ 'func': thru, 'args': [reverse], 'thisArg': undefined }); return new LodashWrapper(wrapped, this.__chain__); } return this.thru(reverse); } /** * Executes the chain sequence to resolve the unwrapped value. * * @name value * @memberOf _ * @since 0.1.0 * @alias toJSON, valueOf * @category Seq * @returns {*} Returns the resolved unwrapped value. * @example * * _([1, 2, 3]).value(); * // => [1, 2, 3] */ function wrapperValue() { return baseWrapperValue(this.__wrapped__, this.__actions__); } /*------------------------------------------------------------------------*/ /** * Creates an object composed of keys generated from the results of running * each element of `collection` thru `iteratee`. The corresponding value of * each key is the number of times the key was returned by `iteratee`. The * iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 0.5.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The iteratee to transform keys. * @returns {Object} Returns the composed aggregate object. * @example * * _.countBy([6.1, 4.2, 6.3], Math.floor); * // => { '4': 1, '6': 2 } * * // The `_.property` iteratee shorthand. * _.countBy(['one', 'two', 'three'], 'length'); * // => { '3': 2, '5': 1 } */ var countBy = createAggregator(function(result, value, key) { if (hasOwnProperty.call(result, key)) { ++result[key]; } else { baseAssignValue(result, key, 1); } }); /** * Checks if `predicate` returns truthy for **all** elements of `collection`. * Iteration is stopped once `predicate` returns falsey. The predicate is * invoked with three arguments: (value, index|key, collection). * * **Note:** This method returns `true` for * [empty collections](https://en.wikipedia.org/wiki/Empty_set) because * [everything is true](https://en.wikipedia.org/wiki/Vacuous_truth) of * elements of empty collections. * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {boolean} Returns `true` if all elements pass the predicate check, * else `false`. * @example * * _.every([true, 1, null, 'yes'], Boolean); * // => false * * var users = [ * { 'user': 'barney', 'age': 36, 'active': false }, * { 'user': 'fred', 'age': 40, 'active': false } * ]; * * // The `_.matches` iteratee shorthand. * _.every(users, { 'user': 'barney', 'active': false }); * // => false * * // The `_.matchesProperty` iteratee shorthand. * _.every(users, ['active', false]); * // => true * * // The `_.property` iteratee shorthand. * _.every(users, 'active'); * // => false */ function every(collection, predicate, guard) { var func = isArray(collection) ? arrayEvery : baseEvery; if (guard && isIterateeCall(collection, predicate, guard)) { predicate = undefined; } return func(collection, getIteratee(predicate, 3)); } /** * Iterates over elements of `collection`, returning an array of all elements * `predicate` returns truthy for. The predicate is invoked with three * arguments: (value, index|key, collection). * * **Note:** Unlike `_.remove`, this method returns a new array. * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the new filtered array. * @see _.reject * @example * * var users = [ * { 'user': 'barney', 'age': 36, 'active': true }, * { 'user': 'fred', 'age': 40, 'active': false } * ]; * * _.filter(users, function(o) { return !o.active; }); * // => objects for ['fred'] * * // The `_.matches` iteratee shorthand. * _.filter(users, { 'age': 36, 'active': true }); * // => objects for ['barney'] * * // The `_.matchesProperty` iteratee shorthand. * _.filter(users, ['active', false]); * // => objects for ['fred'] * * // The `_.property` iteratee shorthand. * _.filter(users, 'active'); * // => objects for ['barney'] */ function filter(collection, predicate) { var func = isArray(collection) ? arrayFilter : baseFilter; return func(collection, getIteratee(predicate, 3)); } /** * Iterates over elements of `collection`, returning the first element * `predicate` returns truthy for. The predicate is invoked with three * arguments: (value, index|key, collection). * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to inspect. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @param {number} [fromIndex=0] The index to search from. * @returns {*} Returns the matched element, else `undefined`. * @example * * var users = [ * { 'user': 'barney', 'age': 36, 'active': true }, * { 'user': 'fred', 'age': 40, 'active': false }, * { 'user': 'pebbles', 'age': 1, 'active': true } * ]; * * _.find(users, function(o) { return o.age < 40; }); * // => object for 'barney' * * // The `_.matches` iteratee shorthand. * _.find(users, { 'age': 1, 'active': true }); * // => object for 'pebbles' * * // The `_.matchesProperty` iteratee shorthand. * _.find(users, ['active', false]); * // => object for 'fred' * * // The `_.property` iteratee shorthand. * _.find(users, 'active'); * // => object for 'barney' */ var find = createFind(findIndex); /** * This method is like `_.find` except that it iterates over elements of * `collection` from right to left. * * @static * @memberOf _ * @since 2.0.0 * @category Collection * @param {Array|Object} collection The collection to inspect. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @param {number} [fromIndex=collection.length-1] The index to search from. * @returns {*} Returns the matched element, else `undefined`. * @example * * _.findLast([1, 2, 3, 4], function(n) { * return n % 2 == 1; * }); * // => 3 */ var findLast = createFind(findLastIndex); /** * Creates a flattened array of values by running each element in `collection` * thru `iteratee` and flattening the mapped results. The iteratee is invoked * with three arguments: (value, index|key, collection). * * @static * @memberOf _ * @since 4.0.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Array} Returns the new flattened array. * @example * * function duplicate(n) { * return [n, n]; * } * * _.flatMap([1, 2], duplicate); * // => [1, 1, 2, 2] */ function flatMap(collection, iteratee) { return baseFlatten(map(collection, iteratee), 1); } /** * This method is like `_.flatMap` except that it recursively flattens the * mapped results. * * @static * @memberOf _ * @since 4.7.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Array} Returns the new flattened array. * @example * * function duplicate(n) { * return [[[n, n]]]; * } * * _.flatMapDeep([1, 2], duplicate); * // => [1, 1, 2, 2] */ function flatMapDeep(collection, iteratee) { return baseFlatten(map(collection, iteratee), INFINITY); } /** * This method is like `_.flatMap` except that it recursively flattens the * mapped results up to `depth` times. * * @static * @memberOf _ * @since 4.7.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @param {number} [depth=1] The maximum recursion depth. * @returns {Array} Returns the new flattened array. * @example * * function duplicate(n) { * return [[[n, n]]]; * } * * _.flatMapDepth([1, 2], duplicate, 2); * // => [[1, 1], [2, 2]] */ function flatMapDepth(collection, iteratee, depth) { depth = depth === undefined ? 1 : toInteger(depth); return baseFlatten(map(collection, iteratee), depth); } /** * Iterates over elements of `collection` and invokes `iteratee` for each element. * The iteratee is invoked with three arguments: (value, index|key, collection). * Iteratee functions may exit iteration early by explicitly returning `false`. * * **Note:** As with other "Collections" methods, objects with a "length" * property are iterated like arrays. To avoid this behavior use `_.forIn` * or `_.forOwn` for object iteration. * * @static * @memberOf _ * @since 0.1.0 * @alias each * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Array|Object} Returns `collection`. * @see _.forEachRight * @example * * _.forEach([1, 2], function(value) { * console.log(value); * }); * // => Logs `1` then `2`. * * _.forEach({ 'a': 1, 'b': 2 }, function(value, key) { * console.log(key); * }); * // => Logs 'a' then 'b' (iteration order is not guaranteed). */ function forEach(collection, iteratee) { var func = isArray(collection) ? arrayEach : baseEach; return func(collection, getIteratee(iteratee, 3)); } /** * This method is like `_.forEach` except that it iterates over elements of * `collection` from right to left. * * @static * @memberOf _ * @since 2.0.0 * @alias eachRight * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Array|Object} Returns `collection`. * @see _.forEach * @example * * _.forEachRight([1, 2], function(value) { * console.log(value); * }); * // => Logs `2` then `1`. */ function forEachRight(collection, iteratee) { var func = isArray(collection) ? arrayEachRight : baseEachRight; return func(collection, getIteratee(iteratee, 3)); } /** * Creates an object composed of keys generated from the results of running * each element of `collection` thru `iteratee`. The order of grouped values * is determined by the order they occur in `collection`. The corresponding * value of each key is an array of elements responsible for generating the * key. The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The iteratee to transform keys. * @returns {Object} Returns the composed aggregate object. * @example * * _.groupBy([6.1, 4.2, 6.3], Math.floor); * // => { '4': [4.2], '6': [6.1, 6.3] } * * // The `_.property` iteratee shorthand. * _.groupBy(['one', 'two', 'three'], 'length'); * // => { '3': ['one', 'two'], '5': ['three'] } */ var groupBy = createAggregator(function(result, value, key) { if (hasOwnProperty.call(result, key)) { result[key].push(value); } else { baseAssignValue(result, key, [value]); } }); /** * Checks if `value` is in `collection`. If `collection` is a string, it's * checked for a substring of `value`, otherwise * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * is used for equality comparisons. If `fromIndex` is negative, it's used as * the offset from the end of `collection`. * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object|string} collection The collection to inspect. * @param {*} value The value to search for. * @param {number} [fromIndex=0] The index to search from. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.reduce`. * @returns {boolean} Returns `true` if `value` is found, else `false`. * @example * * _.includes([1, 2, 3], 1); * // => true * * _.includes([1, 2, 3], 1, 2); * // => false * * _.includes({ 'a': 1, 'b': 2 }, 1); * // => true * * _.includes('abcd', 'bc'); * // => true */ function includes(collection, value, fromIndex, guard) { collection = isArrayLike(collection) ? collection : values(collection); fromIndex = (fromIndex && !guard) ? toInteger(fromIndex) : 0; var length = collection.length; if (fromIndex < 0) { fromIndex = nativeMax(length + fromIndex, 0); } return isString(collection) ? (fromIndex <= length && collection.indexOf(value, fromIndex) > -1) : (!!length && baseIndexOf(collection, value, fromIndex) > -1); } /** * Invokes the method at `path` of each element in `collection`, returning * an array of the results of each invoked method. Any additional arguments * are provided to each invoked method. If `path` is a function, it's invoked * for, and `this` bound to, each element in `collection`. * * @static * @memberOf _ * @since 4.0.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Array|Function|string} path The path of the method to invoke or * the function invoked per iteration. * @param {...*} [args] The arguments to invoke each method with. * @returns {Array} Returns the array of results. * @example * * _.invokeMap([[5, 1, 7], [3, 2, 1]], 'sort'); * // => [[1, 5, 7], [1, 2, 3]] * * _.invokeMap([123, 456], String.prototype.split, ''); * // => [['1', '2', '3'], ['4', '5', '6']] */ var invokeMap = baseRest(function(collection, path, args) { var index = -1, isFunc = typeof path == 'function', result = isArrayLike(collection) ? Array(collection.length) : []; baseEach(collection, function(value) { result[++index] = isFunc ? apply(path, value, args) : baseInvoke(value, path, args); }); return result; }); /** * Creates an object composed of keys generated from the results of running * each element of `collection` thru `iteratee`. The corresponding value of * each key is the last element responsible for generating the key. The * iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The iteratee to transform keys. * @returns {Object} Returns the composed aggregate object. * @example * * var array = [ * { 'dir': 'left', 'code': 97 }, * { 'dir': 'right', 'code': 100 } * ]; * * _.keyBy(array, function(o) { * return String.fromCharCode(o.code); * }); * // => { 'a': { 'dir': 'left', 'code': 97 }, 'd': { 'dir': 'right', 'code': 100 } } * * _.keyBy(array, 'dir'); * // => { 'left': { 'dir': 'left', 'code': 97 }, 'right': { 'dir': 'right', 'code': 100 } } */ var keyBy = createAggregator(function(result, value, key) { baseAssignValue(result, key, value); }); /** * Creates an array of values by running each element in `collection` thru * `iteratee`. The iteratee is invoked with three arguments: * (value, index|key, collection). * * Many lodash methods are guarded to work as iteratees for methods like * `_.every`, `_.filter`, `_.map`, `_.mapValues`, `_.reject`, and `_.some`. * * The guarded methods are: * `ary`, `chunk`, `curry`, `curryRight`, `drop`, `dropRight`, `every`, * `fill`, `invert`, `parseInt`, `random`, `range`, `rangeRight`, `repeat`, * `sampleSize`, `slice`, `some`, `sortBy`, `split`, `take`, `takeRight`, * `template`, `trim`, `trimEnd`, `trimStart`, and `words` * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Array} Returns the new mapped array. * @example * * function square(n) { * return n * n; * } * * _.map([4, 8], square); * // => [16, 64] * * _.map({ 'a': 4, 'b': 8 }, square); * // => [16, 64] (iteration order is not guaranteed) * * var users = [ * { 'user': 'barney' }, * { 'user': 'fred' } * ]; * * // The `_.property` iteratee shorthand. * _.map(users, 'user'); * // => ['barney', 'fred'] */ function map(collection, iteratee) { var func = isArray(collection) ? arrayMap : baseMap; return func(collection, getIteratee(iteratee, 3)); } /** * This method is like `_.sortBy` except that it allows specifying the sort * orders of the iteratees to sort by. If `orders` is unspecified, all values * are sorted in ascending order. Otherwise, specify an order of "desc" for * descending or "asc" for ascending sort order of corresponding values. * * @static * @memberOf _ * @since 4.0.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Array[]|Function[]|Object[]|string[]} [iteratees=[_.identity]] * The iteratees to sort by. * @param {string[]} [orders] The sort orders of `iteratees`. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.reduce`. * @returns {Array} Returns the new sorted array. * @example * * var users = [ * { 'user': 'fred', 'age': 48 }, * { 'user': 'barney', 'age': 34 }, * { 'user': 'fred', 'age': 40 }, * { 'user': 'barney', 'age': 36 } * ]; * * // Sort by `user` in ascending order and by `age` in descending order. * _.orderBy(users, ['user', 'age'], ['asc', 'desc']); * // => objects for [['barney', 36], ['barney', 34], ['fred', 48], ['fred', 40]] */ function orderBy(collection, iteratees, orders, guard) { if (collection == null) { return []; } if (!isArray(iteratees)) { iteratees = iteratees == null ? [] : [iteratees]; } orders = guard ? undefined : orders; if (!isArray(orders)) { orders = orders == null ? [] : [orders]; } return baseOrderBy(collection, iteratees, orders); } /** * Creates an array of elements split into two groups, the first of which * contains elements `predicate` returns truthy for, the second of which * contains elements `predicate` returns falsey for. The predicate is * invoked with one argument: (value). * * @static * @memberOf _ * @since 3.0.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the array of grouped elements. * @example * * var users = [ * { 'user': 'barney', 'age': 36, 'active': false }, * { 'user': 'fred', 'age': 40, 'active': true }, * { 'user': 'pebbles', 'age': 1, 'active': false } * ]; * * _.partition(users, function(o) { return o.active; }); * // => objects for [['fred'], ['barney', 'pebbles']] * * // The `_.matches` iteratee shorthand. * _.partition(users, { 'age': 1, 'active': false }); * // => objects for [['pebbles'], ['barney', 'fred']] * * // The `_.matchesProperty` iteratee shorthand. * _.partition(users, ['active', false]); * // => objects for [['barney', 'pebbles'], ['fred']] * * // The `_.property` iteratee shorthand. * _.partition(users, 'active'); * // => objects for [['fred'], ['barney', 'pebbles']] */ var partition = createAggregator(function(result, value, key) { result[key ? 0 : 1].push(value); }, function() { return [[], []]; }); /** * Reduces `collection` to a value which is the accumulated result of running * each element in `collection` thru `iteratee`, where each successive * invocation is supplied the return value of the previous. If `accumulator` * is not given, the first element of `collection` is used as the initial * value. The iteratee is invoked with four arguments: * (accumulator, value, index|key, collection). * * Many lodash methods are guarded to work as iteratees for methods like * `_.reduce`, `_.reduceRight`, and `_.transform`. * * The guarded methods are: * `assign`, `defaults`, `defaultsDeep`, `includes`, `merge`, `orderBy`, * and `sortBy` * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @param {*} [accumulator] The initial value. * @returns {*} Returns the accumulated value. * @see _.reduceRight * @example * * _.reduce([1, 2], function(sum, n) { * return sum + n; * }, 0); * // => 3 * * _.reduce({ 'a': 1, 'b': 2, 'c': 1 }, function(result, value, key) { * (result[value] || (result[value] = [])).push(key); * return result; * }, {}); * // => { '1': ['a', 'c'], '2': ['b'] } (iteration order is not guaranteed) */ function reduce(collection, iteratee, accumulator) { var func = isArray(collection) ? arrayReduce : baseReduce, initAccum = arguments.length < 3; return func(collection, getIteratee(iteratee, 4), accumulator, initAccum, baseEach); } /** * This method is like `_.reduce` except that it iterates over elements of * `collection` from right to left. * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @param {*} [accumulator] The initial value. * @returns {*} Returns the accumulated value. * @see _.reduce * @example * * var array = [[0, 1], [2, 3], [4, 5]]; * * _.reduceRight(array, function(flattened, other) { * return flattened.concat(other); * }, []); * // => [4, 5, 2, 3, 0, 1] */ function reduceRight(collection, iteratee, accumulator) { var func = isArray(collection) ? arrayReduceRight : baseReduce, initAccum = arguments.length < 3; return func(collection, getIteratee(iteratee, 4), accumulator, initAccum, baseEachRight); } /** * The opposite of `_.filter`; this method returns the elements of `collection` * that `predicate` does **not** return truthy for. * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {Array} Returns the new filtered array. * @see _.filter * @example * * var users = [ * { 'user': 'barney', 'age': 36, 'active': false }, * { 'user': 'fred', 'age': 40, 'active': true } * ]; * * _.reject(users, function(o) { return !o.active; }); * // => objects for ['fred'] * * // The `_.matches` iteratee shorthand. * _.reject(users, { 'age': 40, 'active': true }); * // => objects for ['barney'] * * // The `_.matchesProperty` iteratee shorthand. * _.reject(users, ['active', false]); * // => objects for ['fred'] * * // The `_.property` iteratee shorthand. * _.reject(users, 'active'); * // => objects for ['barney'] */ function reject(collection, predicate) { var func = isArray(collection) ? arrayFilter : baseFilter; return func(collection, negate(getIteratee(predicate, 3))); } /** * Gets a random element from `collection`. * * @static * @memberOf _ * @since 2.0.0 * @category Collection * @param {Array|Object} collection The collection to sample. * @returns {*} Returns the random element. * @example * * _.sample([1, 2, 3, 4]); * // => 2 */ function sample(collection) { var func = isArray(collection) ? arraySample : baseSample; return func(collection); } /** * Gets `n` random elements at unique keys from `collection` up to the * size of `collection`. * * @static * @memberOf _ * @since 4.0.0 * @category Collection * @param {Array|Object} collection The collection to sample. * @param {number} [n=1] The number of elements to sample. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the random elements. * @example * * _.sampleSize([1, 2, 3], 2); * // => [3, 1] * * _.sampleSize([1, 2, 3], 4); * // => [2, 3, 1] */ function sampleSize(collection, n, guard) { if ((guard ? isIterateeCall(collection, n, guard) : n === undefined)) { n = 1; } else { n = toInteger(n); } var func = isArray(collection) ? arraySampleSize : baseSampleSize; return func(collection, n); } /** * Creates an array of shuffled values, using a version of the * [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher-Yates_shuffle). * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to shuffle. * @returns {Array} Returns the new shuffled array. * @example * * _.shuffle([1, 2, 3, 4]); * // => [4, 1, 3, 2] */ function shuffle(collection) { var func = isArray(collection) ? arrayShuffle : baseShuffle; return func(collection); } /** * Gets the size of `collection` by returning its length for array-like * values or the number of own enumerable string keyed properties for objects. * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object|string} collection The collection to inspect. * @returns {number} Returns the collection size. * @example * * _.size([1, 2, 3]); * // => 3 * * _.size({ 'a': 1, 'b': 2 }); * // => 2 * * _.size('pebbles'); * // => 7 */ function size(collection) { if (collection == null) { return 0; } if (isArrayLike(collection)) { return isString(collection) ? stringSize(collection) : collection.length; } var tag = getTag(collection); if (tag == mapTag || tag == setTag) { return collection.size; } return baseKeys(collection).length; } /** * Checks if `predicate` returns truthy for **any** element of `collection`. * Iteration is stopped once `predicate` returns truthy. The predicate is * invoked with three arguments: (value, index|key, collection). * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {boolean} Returns `true` if any element passes the predicate check, * else `false`. * @example * * _.some([null, 0, 'yes', false], Boolean); * // => true * * var users = [ * { 'user': 'barney', 'active': true }, * { 'user': 'fred', 'active': false } * ]; * * // The `_.matches` iteratee shorthand. * _.some(users, { 'user': 'barney', 'active': false }); * // => false * * // The `_.matchesProperty` iteratee shorthand. * _.some(users, ['active', false]); * // => true * * // The `_.property` iteratee shorthand. * _.some(users, 'active'); * // => true */ function some(collection, predicate, guard) { var func = isArray(collection) ? arraySome : baseSome; if (guard && isIterateeCall(collection, predicate, guard)) { predicate = undefined; } return func(collection, getIteratee(predicate, 3)); } /** * Creates an array of elements, sorted in ascending order by the results of * running each element in a collection thru each iteratee. This method * performs a stable sort, that is, it preserves the original sort order of * equal elements. The iteratees are invoked with one argument: (value). * * @static * @memberOf _ * @since 0.1.0 * @category Collection * @param {Array|Object} collection The collection to iterate over. * @param {...(Function|Function[])} [iteratees=[_.identity]] * The iteratees to sort by. * @returns {Array} Returns the new sorted array. * @example * * var users = [ * { 'user': 'fred', 'age': 48 }, * { 'user': 'barney', 'age': 36 }, * { 'user': 'fred', 'age': 40 }, * { 'user': 'barney', 'age': 34 } * ]; * * _.sortBy(users, [function(o) { return o.user; }]); * // => objects for [['barney', 36], ['barney', 34], ['fred', 48], ['fred', 40]] * * _.sortBy(users, ['user', 'age']); * // => objects for [['barney', 34], ['barney', 36], ['fred', 40], ['fred', 48]] */ var sortBy = baseRest(function(collection, iteratees) { if (collection == null) { return []; } var length = iteratees.length; if (length > 1 && isIterateeCall(collection, iteratees[0], iteratees[1])) { iteratees = []; } else if (length > 2 && isIterateeCall(iteratees[0], iteratees[1], iteratees[2])) { iteratees = [iteratees[0]]; } return baseOrderBy(collection, baseFlatten(iteratees, 1), []); }); /*------------------------------------------------------------------------*/ /** * Gets the timestamp of the number of milliseconds that have elapsed since * the Unix epoch (1 January 1970 00:00:00 UTC). * * @static * @memberOf _ * @since 2.4.0 * @category Date * @returns {number} Returns the timestamp. * @example * * _.defer(function(stamp) { * console.log(_.now() - stamp); * }, _.now()); * // => Logs the number of milliseconds it took for the deferred invocation. */ var now = ctxNow || function() { return root.Date.now(); }; /*------------------------------------------------------------------------*/ /** * The opposite of `_.before`; this method creates a function that invokes * `func` once it's called `n` or more times. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {number} n The number of calls before `func` is invoked. * @param {Function} func The function to restrict. * @returns {Function} Returns the new restricted function. * @example * * var saves = ['profile', 'settings']; * * var done = _.after(saves.length, function() { * console.log('done saving!'); * }); * * _.forEach(saves, function(type) { * asyncSave({ 'type': type, 'complete': done }); * }); * // => Logs 'done saving!' after the two async saves have completed. */ function after(n, func) { if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } n = toInteger(n); return function() { if (--n < 1) { return func.apply(this, arguments); } }; } /** * Creates a function that invokes `func`, with up to `n` arguments, * ignoring any additional arguments. * * @static * @memberOf _ * @since 3.0.0 * @category Function * @param {Function} func The function to cap arguments for. * @param {number} [n=func.length] The arity cap. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Function} Returns the new capped function. * @example * * _.map(['6', '8', '10'], _.ary(parseInt, 1)); * // => [6, 8, 10] */ function ary(func, n, guard) { n = guard ? undefined : n; n = (func && n == null) ? func.length : n; return createWrap(func, WRAP_ARY_FLAG, undefined, undefined, undefined, undefined, n); } /** * Creates a function that invokes `func`, with the `this` binding and arguments * of the created function, while it's called less than `n` times. Subsequent * calls to the created function return the result of the last `func` invocation. * * @static * @memberOf _ * @since 3.0.0 * @category Function * @param {number} n The number of calls at which `func` is no longer invoked. * @param {Function} func The function to restrict. * @returns {Function} Returns the new restricted function. * @example * * jQuery(element).on('click', _.before(5, addContactToList)); * // => Allows adding up to 4 contacts to the list. */ function before(n, func) { var result; if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } n = toInteger(n); return function() { if (--n > 0) { result = func.apply(this, arguments); } if (n <= 1) { func = undefined; } return result; }; } /** * Creates a function that invokes `func` with the `this` binding of `thisArg` * and `partials` prepended to the arguments it receives. * * The `_.bind.placeholder` value, which defaults to `_` in monolithic builds, * may be used as a placeholder for partially applied arguments. * * **Note:** Unlike native `Function#bind`, this method doesn't set the "length" * property of bound functions. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to bind. * @param {*} thisArg The `this` binding of `func`. * @param {...*} [partials] The arguments to be partially applied. * @returns {Function} Returns the new bound function. * @example * * function greet(greeting, punctuation) { * return greeting + ' ' + this.user + punctuation; * } * * var object = { 'user': 'fred' }; * * var bound = _.bind(greet, object, 'hi'); * bound('!'); * // => 'hi fred!' * * // Bound with placeholders. * var bound = _.bind(greet, object, _, '!'); * bound('hi'); * // => 'hi fred!' */ var bind = baseRest(function(func, thisArg, partials) { var bitmask = WRAP_BIND_FLAG; if (partials.length) { var holders = replaceHolders(partials, getHolder(bind)); bitmask |= WRAP_PARTIAL_FLAG; } return createWrap(func, bitmask, thisArg, partials, holders); }); /** * Creates a function that invokes the method at `object[key]` with `partials` * prepended to the arguments it receives. * * This method differs from `_.bind` by allowing bound functions to reference * methods that may be redefined or don't yet exist. See * [Peter Michaux's article](http://peter.michaux.ca/articles/lazy-function-definition-pattern) * for more details. * * The `_.bindKey.placeholder` value, which defaults to `_` in monolithic * builds, may be used as a placeholder for partially applied arguments. * * @static * @memberOf _ * @since 0.10.0 * @category Function * @param {Object} object The object to invoke the method on. * @param {string} key The key of the method. * @param {...*} [partials] The arguments to be partially applied. * @returns {Function} Returns the new bound function. * @example * * var object = { * 'user': 'fred', * 'greet': function(greeting, punctuation) { * return greeting + ' ' + this.user + punctuation; * } * }; * * var bound = _.bindKey(object, 'greet', 'hi'); * bound('!'); * // => 'hi fred!' * * object.greet = function(greeting, punctuation) { * return greeting + 'ya ' + this.user + punctuation; * }; * * bound('!'); * // => 'hiya fred!' * * // Bound with placeholders. * var bound = _.bindKey(object, 'greet', _, '!'); * bound('hi'); * // => 'hiya fred!' */ var bindKey = baseRest(function(object, key, partials) { var bitmask = WRAP_BIND_FLAG | WRAP_BIND_KEY_FLAG; if (partials.length) { var holders = replaceHolders(partials, getHolder(bindKey)); bitmask |= WRAP_PARTIAL_FLAG; } return createWrap(key, bitmask, object, partials, holders); }); /** * Creates a function that accepts arguments of `func` and either invokes * `func` returning its result, if at least `arity` number of arguments have * been provided, or returns a function that accepts the remaining `func` * arguments, and so on. The arity of `func` may be specified if `func.length` * is not sufficient. * * The `_.curry.placeholder` value, which defaults to `_` in monolithic builds, * may be used as a placeholder for provided arguments. * * **Note:** This method doesn't set the "length" property of curried functions. * * @static * @memberOf _ * @since 2.0.0 * @category Function * @param {Function} func The function to curry. * @param {number} [arity=func.length] The arity of `func`. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Function} Returns the new curried function. * @example * * var abc = function(a, b, c) { * return [a, b, c]; * }; * * var curried = _.curry(abc); * * curried(1)(2)(3); * // => [1, 2, 3] * * curried(1, 2)(3); * // => [1, 2, 3] * * curried(1, 2, 3); * // => [1, 2, 3] * * // Curried with placeholders. * curried(1)(_, 3)(2); * // => [1, 2, 3] */ function curry(func, arity, guard) { arity = guard ? undefined : arity; var result = createWrap(func, WRAP_CURRY_FLAG, undefined, undefined, undefined, undefined, undefined, arity); result.placeholder = curry.placeholder; return result; } /** * This method is like `_.curry` except that arguments are applied to `func` * in the manner of `_.partialRight` instead of `_.partial`. * * The `_.curryRight.placeholder` value, which defaults to `_` in monolithic * builds, may be used as a placeholder for provided arguments. * * **Note:** This method doesn't set the "length" property of curried functions. * * @static * @memberOf _ * @since 3.0.0 * @category Function * @param {Function} func The function to curry. * @param {number} [arity=func.length] The arity of `func`. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Function} Returns the new curried function. * @example * * var abc = function(a, b, c) { * return [a, b, c]; * }; * * var curried = _.curryRight(abc); * * curried(3)(2)(1); * // => [1, 2, 3] * * curried(2, 3)(1); * // => [1, 2, 3] * * curried(1, 2, 3); * // => [1, 2, 3] * * // Curried with placeholders. * curried(3)(1, _)(2); * // => [1, 2, 3] */ function curryRight(func, arity, guard) { arity = guard ? undefined : arity; var result = createWrap(func, WRAP_CURRY_RIGHT_FLAG, undefined, undefined, undefined, undefined, undefined, arity); result.placeholder = curryRight.placeholder; return result; } /** * Creates a debounced function that delays invoking `func` until after `wait` * milliseconds have elapsed since the last time the debounced function was * invoked. The debounced function comes with a `cancel` method to cancel * delayed `func` invocations and a `flush` method to immediately invoke them. * Provide `options` to indicate whether `func` should be invoked on the * leading and/or trailing edge of the `wait` timeout. The `func` is invoked * with the last arguments provided to the debounced function. Subsequent * calls to the debounced function return the result of the last `func` * invocation. * * **Note:** If `leading` and `trailing` options are `true`, `func` is * invoked on the trailing edge of the timeout only if the debounced function * is invoked more than once during the `wait` timeout. * * If `wait` is `0` and `leading` is `false`, `func` invocation is deferred * until to the next tick, similar to `setTimeout` with a timeout of `0`. * * See [David Corbacho's article](https://css-tricks.com/debouncing-throttling-explained-examples/) * for details over the differences between `_.debounce` and `_.throttle`. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to debounce. * @param {number} [wait=0] The number of milliseconds to delay. * @param {Object} [options={}] The options object. * @param {boolean} [options.leading=false] * Specify invoking on the leading edge of the timeout. * @param {number} [options.maxWait] * The maximum time `func` is allowed to be delayed before it's invoked. * @param {boolean} [options.trailing=true] * Specify invoking on the trailing edge of the timeout. * @returns {Function} Returns the new debounced function. * @example * * // Avoid costly calculations while the window size is in flux. * jQuery(window).on('resize', _.debounce(calculateLayout, 150)); * * // Invoke `sendMail` when clicked, debouncing subsequent calls. * jQuery(element).on('click', _.debounce(sendMail, 300, { * 'leading': true, * 'trailing': false * })); * * // Ensure `batchLog` is invoked once after 1 second of debounced calls. * var debounced = _.debounce(batchLog, 250, { 'maxWait': 1000 }); * var source = new EventSource('/stream'); * jQuery(source).on('message', debounced); * * // Cancel the trailing debounced invocation. * jQuery(window).on('popstate', debounced.cancel); */ function debounce(func, wait, options) { var lastArgs, lastThis, maxWait, result, timerId, lastCallTime, lastInvokeTime = 0, leading = false, maxing = false, trailing = true; if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } wait = toNumber(wait) || 0; if (isObject(options)) { leading = !!options.leading; maxing = 'maxWait' in options; maxWait = maxing ? nativeMax(toNumber(options.maxWait) || 0, wait) : maxWait; trailing = 'trailing' in options ? !!options.trailing : trailing; } function invokeFunc(time) { var args = lastArgs, thisArg = lastThis; lastArgs = lastThis = undefined; lastInvokeTime = time; result = func.apply(thisArg, args); return result; } function leadingEdge(time) { // Reset any `maxWait` timer. lastInvokeTime = time; // Start the timer for the trailing edge. timerId = setTimeout(timerExpired, wait); // Invoke the leading edge. return leading ? invokeFunc(time) : result; } function remainingWait(time) { var timeSinceLastCall = time - lastCallTime, timeSinceLastInvoke = time - lastInvokeTime, result = wait - timeSinceLastCall; return maxing ? nativeMin(result, maxWait - timeSinceLastInvoke) : result; } function shouldInvoke(time) { var timeSinceLastCall = time - lastCallTime, timeSinceLastInvoke = time - lastInvokeTime; // Either this is the first call, activity has stopped and we're at the // trailing edge, the system time has gone backwards and we're treating // it as the trailing edge, or we've hit the `maxWait` limit. return (lastCallTime === undefined || (timeSinceLastCall >= wait) || (timeSinceLastCall < 0) || (maxing && timeSinceLastInvoke >= maxWait)); } function timerExpired() { var time = now(); if (shouldInvoke(time)) { return trailingEdge(time); } // Restart the timer. timerId = setTimeout(timerExpired, remainingWait(time)); } function trailingEdge(time) { timerId = undefined; // Only invoke if we have `lastArgs` which means `func` has been // debounced at least once. if (trailing && lastArgs) { return invokeFunc(time); } lastArgs = lastThis = undefined; return result; } function cancel() { if (timerId !== undefined) { clearTimeout(timerId); } lastInvokeTime = 0; lastArgs = lastCallTime = lastThis = timerId = undefined; } function flush() { return timerId === undefined ? result : trailingEdge(now()); } function debounced() { var time = now(), isInvoking = shouldInvoke(time); lastArgs = arguments; lastThis = this; lastCallTime = time; if (isInvoking) { if (timerId === undefined) { return leadingEdge(lastCallTime); } if (maxing) { // Handle invocations in a tight loop. timerId = setTimeout(timerExpired, wait); return invokeFunc(lastCallTime); } } if (timerId === undefined) { timerId = setTimeout(timerExpired, wait); } return result; } debounced.cancel = cancel; debounced.flush = flush; return debounced; } /** * Defers invoking the `func` until the current call stack has cleared. Any * additional arguments are provided to `func` when it's invoked. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to defer. * @param {...*} [args] The arguments to invoke `func` with. * @returns {number} Returns the timer id. * @example * * _.defer(function(text) { * console.log(text); * }, 'deferred'); * // => Logs 'deferred' after one millisecond. */ var defer = baseRest(function(func, args) { return baseDelay(func, 1, args); }); /** * Invokes `func` after `wait` milliseconds. Any additional arguments are * provided to `func` when it's invoked. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to delay. * @param {number} wait The number of milliseconds to delay invocation. * @param {...*} [args] The arguments to invoke `func` with. * @returns {number} Returns the timer id. * @example * * _.delay(function(text) { * console.log(text); * }, 1000, 'later'); * // => Logs 'later' after one second. */ var delay = baseRest(function(func, wait, args) { return baseDelay(func, toNumber(wait) || 0, args); }); /** * Creates a function that invokes `func` with arguments reversed. * * @static * @memberOf _ * @since 4.0.0 * @category Function * @param {Function} func The function to flip arguments for. * @returns {Function} Returns the new flipped function. * @example * * var flipped = _.flip(function() { * return _.toArray(arguments); * }); * * flipped('a', 'b', 'c', 'd'); * // => ['d', 'c', 'b', 'a'] */ function flip(func) { return createWrap(func, WRAP_FLIP_FLAG); } /** * Creates a function that memoizes the result of `func`. If `resolver` is * provided, it determines the cache key for storing the result based on the * arguments provided to the memoized function. By default, the first argument * provided to the memoized function is used as the map cache key. The `func` * is invoked with the `this` binding of the memoized function. * * **Note:** The cache is exposed as the `cache` property on the memoized * function. Its creation may be customized by replacing the `_.memoize.Cache` * constructor with one whose instances implement the * [`Map`](http://ecma-international.org/ecma-262/7.0/#sec-properties-of-the-map-prototype-object) * method interface of `clear`, `delete`, `get`, `has`, and `set`. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to have its output memoized. * @param {Function} [resolver] The function to resolve the cache key. * @returns {Function} Returns the new memoized function. * @example * * var object = { 'a': 1, 'b': 2 }; * var other = { 'c': 3, 'd': 4 }; * * var values = _.memoize(_.values); * values(object); * // => [1, 2] * * values(other); * // => [3, 4] * * object.a = 2; * values(object); * // => [1, 2] * * // Modify the result cache. * values.cache.set(object, ['a', 'b']); * values(object); * // => ['a', 'b'] * * // Replace `_.memoize.Cache`. * _.memoize.Cache = WeakMap; */ function memoize(func, resolver) { if (typeof func != 'function' || (resolver != null && typeof resolver != 'function')) { throw new TypeError(FUNC_ERROR_TEXT); } var memoized = function() { var args = arguments, key = resolver ? resolver.apply(this, args) : args[0], cache = memoized.cache; if (cache.has(key)) { return cache.get(key); } var result = func.apply(this, args); memoized.cache = cache.set(key, result) || cache; return result; }; memoized.cache = new (memoize.Cache || MapCache); return memoized; } // Expose `MapCache`. memoize.Cache = MapCache; /** * Creates a function that negates the result of the predicate `func`. The * `func` predicate is invoked with the `this` binding and arguments of the * created function. * * @static * @memberOf _ * @since 3.0.0 * @category Function * @param {Function} predicate The predicate to negate. * @returns {Function} Returns the new negated function. * @example * * function isEven(n) { * return n % 2 == 0; * } * * _.filter([1, 2, 3, 4, 5, 6], _.negate(isEven)); * // => [1, 3, 5] */ function negate(predicate) { if (typeof predicate != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } return function() { var args = arguments; switch (args.length) { case 0: return !predicate.call(this); case 1: return !predicate.call(this, args[0]); case 2: return !predicate.call(this, args[0], args[1]); case 3: return !predicate.call(this, args[0], args[1], args[2]); } return !predicate.apply(this, args); }; } /** * Creates a function that is restricted to invoking `func` once. Repeat calls * to the function return the value of the first invocation. The `func` is * invoked with the `this` binding and arguments of the created function. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to restrict. * @returns {Function} Returns the new restricted function. * @example * * var initialize = _.once(createApplication); * initialize(); * initialize(); * // => `createApplication` is invoked once */ function once(func) { return before(2, func); } /** * Creates a function that invokes `func` with its arguments transformed. * * @static * @since 4.0.0 * @memberOf _ * @category Function * @param {Function} func The function to wrap. * @param {...(Function|Function[])} [transforms=[_.identity]] * The argument transforms. * @returns {Function} Returns the new function. * @example * * function doubled(n) { * return n * 2; * } * * function square(n) { * return n * n; * } * * var func = _.overArgs(function(x, y) { * return [x, y]; * }, [square, doubled]); * * func(9, 3); * // => [81, 6] * * func(10, 5); * // => [100, 10] */ var overArgs = castRest(function(func, transforms) { transforms = (transforms.length == 1 && isArray(transforms[0])) ? arrayMap(transforms[0], baseUnary(getIteratee())) : arrayMap(baseFlatten(transforms, 1), baseUnary(getIteratee())); var funcsLength = transforms.length; return baseRest(function(args) { var index = -1, length = nativeMin(args.length, funcsLength); while (++index < length) { args[index] = transforms[index].call(this, args[index]); } return apply(func, this, args); }); }); /** * Creates a function that invokes `func` with `partials` prepended to the * arguments it receives. This method is like `_.bind` except it does **not** * alter the `this` binding. * * The `_.partial.placeholder` value, which defaults to `_` in monolithic * builds, may be used as a placeholder for partially applied arguments. * * **Note:** This method doesn't set the "length" property of partially * applied functions. * * @static * @memberOf _ * @since 0.2.0 * @category Function * @param {Function} func The function to partially apply arguments to. * @param {...*} [partials] The arguments to be partially applied. * @returns {Function} Returns the new partially applied function. * @example * * function greet(greeting, name) { * return greeting + ' ' + name; * } * * var sayHelloTo = _.partial(greet, 'hello'); * sayHelloTo('fred'); * // => 'hello fred' * * // Partially applied with placeholders. * var greetFred = _.partial(greet, _, 'fred'); * greetFred('hi'); * // => 'hi fred' */ var partial = baseRest(function(func, partials) { var holders = replaceHolders(partials, getHolder(partial)); return createWrap(func, WRAP_PARTIAL_FLAG, undefined, partials, holders); }); /** * This method is like `_.partial` except that partially applied arguments * are appended to the arguments it receives. * * The `_.partialRight.placeholder` value, which defaults to `_` in monolithic * builds, may be used as a placeholder for partially applied arguments. * * **Note:** This method doesn't set the "length" property of partially * applied functions. * * @static * @memberOf _ * @since 1.0.0 * @category Function * @param {Function} func The function to partially apply arguments to. * @param {...*} [partials] The arguments to be partially applied. * @returns {Function} Returns the new partially applied function. * @example * * function greet(greeting, name) { * return greeting + ' ' + name; * } * * var greetFred = _.partialRight(greet, 'fred'); * greetFred('hi'); * // => 'hi fred' * * // Partially applied with placeholders. * var sayHelloTo = _.partialRight(greet, 'hello', _); * sayHelloTo('fred'); * // => 'hello fred' */ var partialRight = baseRest(function(func, partials) { var holders = replaceHolders(partials, getHolder(partialRight)); return createWrap(func, WRAP_PARTIAL_RIGHT_FLAG, undefined, partials, holders); }); /** * Creates a function that invokes `func` with arguments arranged according * to the specified `indexes` where the argument value at the first index is * provided as the first argument, the argument value at the second index is * provided as the second argument, and so on. * * @static * @memberOf _ * @since 3.0.0 * @category Function * @param {Function} func The function to rearrange arguments for. * @param {...(number|number[])} indexes The arranged argument indexes. * @returns {Function} Returns the new function. * @example * * var rearged = _.rearg(function(a, b, c) { * return [a, b, c]; * }, [2, 0, 1]); * * rearged('b', 'c', 'a') * // => ['a', 'b', 'c'] */ var rearg = flatRest(function(func, indexes) { return createWrap(func, WRAP_REARG_FLAG, undefined, undefined, undefined, indexes); }); /** * Creates a function that invokes `func` with the `this` binding of the * created function and arguments from `start` and beyond provided as * an array. * * **Note:** This method is based on the * [rest parameter](https://mdn.io/rest_parameters). * * @static * @memberOf _ * @since 4.0.0 * @category Function * @param {Function} func The function to apply a rest parameter to. * @param {number} [start=func.length-1] The start position of the rest parameter. * @returns {Function} Returns the new function. * @example * * var say = _.rest(function(what, names) { * return what + ' ' + _.initial(names).join(', ') + * (_.size(names) > 1 ? ', & ' : '') + _.last(names); * }); * * say('hello', 'fred', 'barney', 'pebbles'); * // => 'hello fred, barney, & pebbles' */ function rest(func, start) { if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } start = start === undefined ? start : toInteger(start); return baseRest(func, start); } /** * Creates a function that invokes `func` with the `this` binding of the * create function and an array of arguments much like * [`Function#apply`](http://www.ecma-international.org/ecma-262/7.0/#sec-function.prototype.apply). * * **Note:** This method is based on the * [spread operator](https://mdn.io/spread_operator). * * @static * @memberOf _ * @since 3.2.0 * @category Function * @param {Function} func The function to spread arguments over. * @param {number} [start=0] The start position of the spread. * @returns {Function} Returns the new function. * @example * * var say = _.spread(function(who, what) { * return who + ' says ' + what; * }); * * say(['fred', 'hello']); * // => 'fred says hello' * * var numbers = Promise.all([ * Promise.resolve(40), * Promise.resolve(36) * ]); * * numbers.then(_.spread(function(x, y) { * return x + y; * })); * // => a Promise of 76 */ function spread(func, start) { if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } start = start == null ? 0 : nativeMax(toInteger(start), 0); return baseRest(function(args) { var array = args[start], otherArgs = castSlice(args, 0, start); if (array) { arrayPush(otherArgs, array); } return apply(func, this, otherArgs); }); } /** * Creates a throttled function that only invokes `func` at most once per * every `wait` milliseconds. The throttled function comes with a `cancel` * method to cancel delayed `func` invocations and a `flush` method to * immediately invoke them. Provide `options` to indicate whether `func` * should be invoked on the leading and/or trailing edge of the `wait` * timeout. The `func` is invoked with the last arguments provided to the * throttled function. Subsequent calls to the throttled function return the * result of the last `func` invocation. * * **Note:** If `leading` and `trailing` options are `true`, `func` is * invoked on the trailing edge of the timeout only if the throttled function * is invoked more than once during the `wait` timeout. * * If `wait` is `0` and `leading` is `false`, `func` invocation is deferred * until to the next tick, similar to `setTimeout` with a timeout of `0`. * * See [David Corbacho's article](https://css-tricks.com/debouncing-throttling-explained-examples/) * for details over the differences between `_.throttle` and `_.debounce`. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {Function} func The function to throttle. * @param {number} [wait=0] The number of milliseconds to throttle invocations to. * @param {Object} [options={}] The options object. * @param {boolean} [options.leading=true] * Specify invoking on the leading edge of the timeout. * @param {boolean} [options.trailing=true] * Specify invoking on the trailing edge of the timeout. * @returns {Function} Returns the new throttled function. * @example * * // Avoid excessively updating the position while scrolling. * jQuery(window).on('scroll', _.throttle(updatePosition, 100)); * * // Invoke `renewToken` when the click event is fired, but not more than once every 5 minutes. * var throttled = _.throttle(renewToken, 300000, { 'trailing': false }); * jQuery(element).on('click', throttled); * * // Cancel the trailing throttled invocation. * jQuery(window).on('popstate', throttled.cancel); */ function throttle(func, wait, options) { var leading = true, trailing = true; if (typeof func != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } if (isObject(options)) { leading = 'leading' in options ? !!options.leading : leading; trailing = 'trailing' in options ? !!options.trailing : trailing; } return debounce(func, wait, { 'leading': leading, 'maxWait': wait, 'trailing': trailing }); } /** * Creates a function that accepts up to one argument, ignoring any * additional arguments. * * @static * @memberOf _ * @since 4.0.0 * @category Function * @param {Function} func The function to cap arguments for. * @returns {Function} Returns the new capped function. * @example * * _.map(['6', '8', '10'], _.unary(parseInt)); * // => [6, 8, 10] */ function unary(func) { return ary(func, 1); } /** * Creates a function that provides `value` to `wrapper` as its first * argument. Any additional arguments provided to the function are appended * to those provided to the `wrapper`. The wrapper is invoked with the `this` * binding of the created function. * * @static * @memberOf _ * @since 0.1.0 * @category Function * @param {*} value The value to wrap. * @param {Function} [wrapper=identity] The wrapper function. * @returns {Function} Returns the new function. * @example * * var p = _.wrap(_.escape, function(func, text) { * return '<p>' + func(text) + '</p>'; * }); * * p('fred, barney, & pebbles'); * // => '<p>fred, barney, & pebbles</p>' */ function wrap(value, wrapper) { return partial(castFunction(wrapper), value); } /*------------------------------------------------------------------------*/ /** * Casts `value` as an array if it's not one. * * @static * @memberOf _ * @since 4.4.0 * @category Lang * @param {*} value The value to inspect. * @returns {Array} Returns the cast array. * @example * * _.castArray(1); * // => [1] * * _.castArray({ 'a': 1 }); * // => [{ 'a': 1 }] * * _.castArray('abc'); * // => ['abc'] * * _.castArray(null); * // => [null] * * _.castArray(undefined); * // => [undefined] * * _.castArray(); * // => [] * * var array = [1, 2, 3]; * console.log(_.castArray(array) === array); * // => true */ function castArray() { if (!arguments.length) { return []; } var value = arguments[0]; return isArray(value) ? value : [value]; } /** * Creates a shallow clone of `value`. * * **Note:** This method is loosely based on the * [structured clone algorithm](https://mdn.io/Structured_clone_algorithm) * and supports cloning arrays, array buffers, booleans, date objects, maps, * numbers, `Object` objects, regexes, sets, strings, symbols, and typed * arrays. The own enumerable properties of `arguments` objects are cloned * as plain objects. An empty object is returned for uncloneable values such * as error objects, functions, DOM nodes, and WeakMaps. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to clone. * @returns {*} Returns the cloned value. * @see _.cloneDeep * @example * * var objects = [{ 'a': 1 }, { 'b': 2 }]; * * var shallow = _.clone(objects); * console.log(shallow[0] === objects[0]); * // => true */ function clone(value) { return baseClone(value, CLONE_SYMBOLS_FLAG); } /** * This method is like `_.clone` except that it accepts `customizer` which * is invoked to produce the cloned value. If `customizer` returns `undefined`, * cloning is handled by the method instead. The `customizer` is invoked with * up to four arguments; (value [, index|key, object, stack]). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to clone. * @param {Function} [customizer] The function to customize cloning. * @returns {*} Returns the cloned value. * @see _.cloneDeepWith * @example * * function customizer(value) { * if (_.isElement(value)) { * return value.cloneNode(false); * } * } * * var el = _.cloneWith(document.body, customizer); * * console.log(el === document.body); * // => false * console.log(el.nodeName); * // => 'BODY' * console.log(el.childNodes.length); * // => 0 */ function cloneWith(value, customizer) { customizer = typeof customizer == 'function' ? customizer : undefined; return baseClone(value, CLONE_SYMBOLS_FLAG, customizer); } /** * This method is like `_.clone` except that it recursively clones `value`. * * @static * @memberOf _ * @since 1.0.0 * @category Lang * @param {*} value The value to recursively clone. * @returns {*} Returns the deep cloned value. * @see _.clone * @example * * var objects = [{ 'a': 1 }, { 'b': 2 }]; * * var deep = _.cloneDeep(objects); * console.log(deep[0] === objects[0]); * // => false */ function cloneDeep(value) { return baseClone(value, CLONE_DEEP_FLAG | CLONE_SYMBOLS_FLAG); } /** * This method is like `_.cloneWith` except that it recursively clones `value`. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to recursively clone. * @param {Function} [customizer] The function to customize cloning. * @returns {*} Returns the deep cloned value. * @see _.cloneWith * @example * * function customizer(value) { * if (_.isElement(value)) { * return value.cloneNode(true); * } * } * * var el = _.cloneDeepWith(document.body, customizer); * * console.log(el === document.body); * // => false * console.log(el.nodeName); * // => 'BODY' * console.log(el.childNodes.length); * // => 20 */ function cloneDeepWith(value, customizer) { customizer = typeof customizer == 'function' ? customizer : undefined; return baseClone(value, CLONE_DEEP_FLAG | CLONE_SYMBOLS_FLAG, customizer); } /** * Checks if `object` conforms to `source` by invoking the predicate * properties of `source` with the corresponding property values of `object`. * * **Note:** This method is equivalent to `_.conforms` when `source` is * partially applied. * * @static * @memberOf _ * @since 4.14.0 * @category Lang * @param {Object} object The object to inspect. * @param {Object} source The object of property predicates to conform to. * @returns {boolean} Returns `true` if `object` conforms, else `false`. * @example * * var object = { 'a': 1, 'b': 2 }; * * _.conformsTo(object, { 'b': function(n) { return n > 1; } }); * // => true * * _.conformsTo(object, { 'b': function(n) { return n > 2; } }); * // => false */ function conformsTo(object, source) { return source == null || baseConformsTo(object, source, keys(source)); } /** * Performs a * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) * comparison between two values to determine if they are equivalent. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if the values are equivalent, else `false`. * @example * * var object = { 'a': 1 }; * var other = { 'a': 1 }; * * _.eq(object, object); * // => true * * _.eq(object, other); * // => false * * _.eq('a', 'a'); * // => true * * _.eq('a', Object('a')); * // => false * * _.eq(NaN, NaN); * // => true */ function eq(value, other) { return value === other || (value !== value && other !== other); } /** * Checks if `value` is greater than `other`. * * @static * @memberOf _ * @since 3.9.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if `value` is greater than `other`, * else `false`. * @see _.lt * @example * * _.gt(3, 1); * // => true * * _.gt(3, 3); * // => false * * _.gt(1, 3); * // => false */ var gt = createRelationalOperation(baseGt); /** * Checks if `value` is greater than or equal to `other`. * * @static * @memberOf _ * @since 3.9.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if `value` is greater than or equal to * `other`, else `false`. * @see _.lte * @example * * _.gte(3, 1); * // => true * * _.gte(3, 3); * // => true * * _.gte(1, 3); * // => false */ var gte = createRelationalOperation(function(value, other) { return value >= other; }); /** * Checks if `value` is likely an `arguments` object. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an `arguments` object, * else `false`. * @example * * _.isArguments(function() { return arguments; }()); * // => true * * _.isArguments([1, 2, 3]); * // => false */ var isArguments = baseIsArguments(function() { return arguments; }()) ? baseIsArguments : function(value) { return isObjectLike(value) && hasOwnProperty.call(value, 'callee') && !propertyIsEnumerable.call(value, 'callee'); }; /** * Checks if `value` is classified as an `Array` object. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an array, else `false`. * @example * * _.isArray([1, 2, 3]); * // => true * * _.isArray(document.body.children); * // => false * * _.isArray('abc'); * // => false * * _.isArray(_.noop); * // => false */ var isArray = Array.isArray; /** * Checks if `value` is classified as an `ArrayBuffer` object. * * @static * @memberOf _ * @since 4.3.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an array buffer, else `false`. * @example * * _.isArrayBuffer(new ArrayBuffer(2)); * // => true * * _.isArrayBuffer(new Array(2)); * // => false */ var isArrayBuffer = nodeIsArrayBuffer ? baseUnary(nodeIsArrayBuffer) : baseIsArrayBuffer; /** * Checks if `value` is array-like. A value is considered array-like if it's * not a function and has a `value.length` that's an integer greater than or * equal to `0` and less than or equal to `Number.MAX_SAFE_INTEGER`. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is array-like, else `false`. * @example * * _.isArrayLike([1, 2, 3]); * // => true * * _.isArrayLike(document.body.children); * // => true * * _.isArrayLike('abc'); * // => true * * _.isArrayLike(_.noop); * // => false */ function isArrayLike(value) { return value != null && isLength(value.length) && !isFunction(value); } /** * This method is like `_.isArrayLike` except that it also checks if `value` * is an object. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an array-like object, * else `false`. * @example * * _.isArrayLikeObject([1, 2, 3]); * // => true * * _.isArrayLikeObject(document.body.children); * // => true * * _.isArrayLikeObject('abc'); * // => false * * _.isArrayLikeObject(_.noop); * // => false */ function isArrayLikeObject(value) { return isObjectLike(value) && isArrayLike(value); } /** * Checks if `value` is classified as a boolean primitive or object. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a boolean, else `false`. * @example * * _.isBoolean(false); * // => true * * _.isBoolean(null); * // => false */ function isBoolean(value) { return value === true || value === false || (isObjectLike(value) && baseGetTag(value) == boolTag); } /** * Checks if `value` is a buffer. * * @static * @memberOf _ * @since 4.3.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a buffer, else `false`. * @example * * _.isBuffer(new Buffer(2)); * // => true * * _.isBuffer(new Uint8Array(2)); * // => false */ var isBuffer = nativeIsBuffer || stubFalse; /** * Checks if `value` is classified as a `Date` object. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a date object, else `false`. * @example * * _.isDate(new Date); * // => true * * _.isDate('Mon April 23 2012'); * // => false */ var isDate = nodeIsDate ? baseUnary(nodeIsDate) : baseIsDate; /** * Checks if `value` is likely a DOM element. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a DOM element, else `false`. * @example * * _.isElement(document.body); * // => true * * _.isElement('<body>'); * // => false */ function isElement(value) { return isObjectLike(value) && value.nodeType === 1 && !isPlainObject(value); } /** * Checks if `value` is an empty object, collection, map, or set. * * Objects are considered empty if they have no own enumerable string keyed * properties. * * Array-like values such as `arguments` objects, arrays, buffers, strings, or * jQuery-like collections are considered empty if they have a `length` of `0`. * Similarly, maps and sets are considered empty if they have a `size` of `0`. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is empty, else `false`. * @example * * _.isEmpty(null); * // => true * * _.isEmpty(true); * // => true * * _.isEmpty(1); * // => true * * _.isEmpty([1, 2, 3]); * // => false * * _.isEmpty({ 'a': 1 }); * // => false */ function isEmpty(value) { if (value == null) { return true; } if (isArrayLike(value) && (isArray(value) || typeof value == 'string' || typeof value.splice == 'function' || isBuffer(value) || isTypedArray(value) || isArguments(value))) { return !value.length; } var tag = getTag(value); if (tag == mapTag || tag == setTag) { return !value.size; } if (isPrototype(value)) { return !baseKeys(value).length; } for (var key in value) { if (hasOwnProperty.call(value, key)) { return false; } } return true; } /** * Performs a deep comparison between two values to determine if they are * equivalent. * * **Note:** This method supports comparing arrays, array buffers, booleans, * date objects, error objects, maps, numbers, `Object` objects, regexes, * sets, strings, symbols, and typed arrays. `Object` objects are compared * by their own, not inherited, enumerable properties. Functions and DOM * nodes are compared by strict equality, i.e. `===`. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if the values are equivalent, else `false`. * @example * * var object = { 'a': 1 }; * var other = { 'a': 1 }; * * _.isEqual(object, other); * // => true * * object === other; * // => false */ function isEqual(value, other) { return baseIsEqual(value, other); } /** * This method is like `_.isEqual` except that it accepts `customizer` which * is invoked to compare values. If `customizer` returns `undefined`, comparisons * are handled by the method instead. The `customizer` is invoked with up to * six arguments: (objValue, othValue [, index|key, object, other, stack]). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @param {Function} [customizer] The function to customize comparisons. * @returns {boolean} Returns `true` if the values are equivalent, else `false`. * @example * * function isGreeting(value) { * return /^h(?:i|ello)$/.test(value); * } * * function customizer(objValue, othValue) { * if (isGreeting(objValue) && isGreeting(othValue)) { * return true; * } * } * * var array = ['hello', 'goodbye']; * var other = ['hi', 'goodbye']; * * _.isEqualWith(array, other, customizer); * // => true */ function isEqualWith(value, other, customizer) { customizer = typeof customizer == 'function' ? customizer : undefined; var result = customizer ? customizer(value, other) : undefined; return result === undefined ? baseIsEqual(value, other, undefined, customizer) : !!result; } /** * Checks if `value` is an `Error`, `EvalError`, `RangeError`, `ReferenceError`, * `SyntaxError`, `TypeError`, or `URIError` object. * * @static * @memberOf _ * @since 3.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an error object, else `false`. * @example * * _.isError(new Error); * // => true * * _.isError(Error); * // => false */ function isError(value) { if (!isObjectLike(value)) { return false; } var tag = baseGetTag(value); return tag == errorTag || tag == domExcTag || (typeof value.message == 'string' && typeof value.name == 'string' && !isPlainObject(value)); } /** * Checks if `value` is a finite primitive number. * * **Note:** This method is based on * [`Number.isFinite`](https://mdn.io/Number/isFinite). * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a finite number, else `false`. * @example * * _.isFinite(3); * // => true * * _.isFinite(Number.MIN_VALUE); * // => true * * _.isFinite(Infinity); * // => false * * _.isFinite('3'); * // => false */ function isFinite(value) { return typeof value == 'number' && nativeIsFinite(value); } /** * Checks if `value` is classified as a `Function` object. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a function, else `false`. * @example * * _.isFunction(_); * // => true * * _.isFunction(/abc/); * // => false */ function isFunction(value) { if (!isObject(value)) { return false; } // The use of `Object#toString` avoids issues with the `typeof` operator // in Safari 9 which returns 'object' for typed arrays and other constructors. var tag = baseGetTag(value); return tag == funcTag || tag == genTag || tag == asyncTag || tag == proxyTag; } /** * Checks if `value` is an integer. * * **Note:** This method is based on * [`Number.isInteger`](https://mdn.io/Number/isInteger). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an integer, else `false`. * @example * * _.isInteger(3); * // => true * * _.isInteger(Number.MIN_VALUE); * // => false * * _.isInteger(Infinity); * // => false * * _.isInteger('3'); * // => false */ function isInteger(value) { return typeof value == 'number' && value == toInteger(value); } /** * Checks if `value` is a valid array-like length. * * **Note:** This method is loosely based on * [`ToLength`](http://ecma-international.org/ecma-262/7.0/#sec-tolength). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a valid length, else `false`. * @example * * _.isLength(3); * // => true * * _.isLength(Number.MIN_VALUE); * // => false * * _.isLength(Infinity); * // => false * * _.isLength('3'); * // => false */ function isLength(value) { return typeof value == 'number' && value > -1 && value % 1 == 0 && value <= MAX_SAFE_INTEGER; } /** * Checks if `value` is the * [language type](http://www.ecma-international.org/ecma-262/7.0/#sec-ecmascript-language-types) * of `Object`. (e.g. arrays, functions, objects, regexes, `new Number(0)`, and `new String('')`) * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is an object, else `false`. * @example * * _.isObject({}); * // => true * * _.isObject([1, 2, 3]); * // => true * * _.isObject(_.noop); * // => true * * _.isObject(null); * // => false */ function isObject(value) { var type = typeof value; return value != null && (type == 'object' || type == 'function'); } /** * Checks if `value` is object-like. A value is object-like if it's not `null` * and has a `typeof` result of "object". * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is object-like, else `false`. * @example * * _.isObjectLike({}); * // => true * * _.isObjectLike([1, 2, 3]); * // => true * * _.isObjectLike(_.noop); * // => false * * _.isObjectLike(null); * // => false */ function isObjectLike(value) { return value != null && typeof value == 'object'; } /** * Checks if `value` is classified as a `Map` object. * * @static * @memberOf _ * @since 4.3.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a map, else `false`. * @example * * _.isMap(new Map); * // => true * * _.isMap(new WeakMap); * // => false */ var isMap = nodeIsMap ? baseUnary(nodeIsMap) : baseIsMap; /** * Performs a partial deep comparison between `object` and `source` to * determine if `object` contains equivalent property values. * * **Note:** This method is equivalent to `_.matches` when `source` is * partially applied. * * Partial comparisons will match empty array and empty object `source` * values against any array or object value, respectively. See `_.isEqual` * for a list of supported value comparisons. * * @static * @memberOf _ * @since 3.0.0 * @category Lang * @param {Object} object The object to inspect. * @param {Object} source The object of property values to match. * @returns {boolean} Returns `true` if `object` is a match, else `false`. * @example * * var object = { 'a': 1, 'b': 2 }; * * _.isMatch(object, { 'b': 2 }); * // => true * * _.isMatch(object, { 'b': 1 }); * // => false */ function isMatch(object, source) { return object === source || baseIsMatch(object, source, getMatchData(source)); } /** * This method is like `_.isMatch` except that it accepts `customizer` which * is invoked to compare values. If `customizer` returns `undefined`, comparisons * are handled by the method instead. The `customizer` is invoked with five * arguments: (objValue, srcValue, index|key, object, source). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {Object} object The object to inspect. * @param {Object} source The object of property values to match. * @param {Function} [customizer] The function to customize comparisons. * @returns {boolean} Returns `true` if `object` is a match, else `false`. * @example * * function isGreeting(value) { * return /^h(?:i|ello)$/.test(value); * } * * function customizer(objValue, srcValue) { * if (isGreeting(objValue) && isGreeting(srcValue)) { * return true; * } * } * * var object = { 'greeting': 'hello' }; * var source = { 'greeting': 'hi' }; * * _.isMatchWith(object, source, customizer); * // => true */ function isMatchWith(object, source, customizer) { customizer = typeof customizer == 'function' ? customizer : undefined; return baseIsMatch(object, source, getMatchData(source), customizer); } /** * Checks if `value` is `NaN`. * * **Note:** This method is based on * [`Number.isNaN`](https://mdn.io/Number/isNaN) and is not the same as * global [`isNaN`](https://mdn.io/isNaN) which returns `true` for * `undefined` and other non-number values. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is `NaN`, else `false`. * @example * * _.isNaN(NaN); * // => true * * _.isNaN(new Number(NaN)); * // => true * * isNaN(undefined); * // => true * * _.isNaN(undefined); * // => false */ function isNaN(value) { // An `NaN` primitive is the only value that is not equal to itself. // Perform the `toStringTag` check first to avoid errors with some // ActiveX objects in IE. return isNumber(value) && value != +value; } /** * Checks if `value` is a pristine native function. * * **Note:** This method can't reliably detect native functions in the presence * of the core-js package because core-js circumvents this kind of detection. * Despite multiple requests, the core-js maintainer has made it clear: any * attempt to fix the detection will be obstructed. As a result, we're left * with little choice but to throw an error. Unfortunately, this also affects * packages, like [babel-polyfill](https://www.npmjs.com/package/babel-polyfill), * which rely on core-js. * * @static * @memberOf _ * @since 3.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a native function, * else `false`. * @example * * _.isNative(Array.prototype.push); * // => true * * _.isNative(_); * // => false */ function isNative(value) { if (isMaskable(value)) { throw new Error(CORE_ERROR_TEXT); } return baseIsNative(value); } /** * Checks if `value` is `null`. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is `null`, else `false`. * @example * * _.isNull(null); * // => true * * _.isNull(void 0); * // => false */ function isNull(value) { return value === null; } /** * Checks if `value` is `null` or `undefined`. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is nullish, else `false`. * @example * * _.isNil(null); * // => true * * _.isNil(void 0); * // => true * * _.isNil(NaN); * // => false */ function isNil(value) { return value == null; } /** * Checks if `value` is classified as a `Number` primitive or object. * * **Note:** To exclude `Infinity`, `-Infinity`, and `NaN`, which are * classified as numbers, use the `_.isFinite` method. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a number, else `false`. * @example * * _.isNumber(3); * // => true * * _.isNumber(Number.MIN_VALUE); * // => true * * _.isNumber(Infinity); * // => true * * _.isNumber('3'); * // => false */ function isNumber(value) { return typeof value == 'number' || (isObjectLike(value) && baseGetTag(value) == numberTag); } /** * Checks if `value` is a plain object, that is, an object created by the * `Object` constructor or one with a `[[Prototype]]` of `null`. * * @static * @memberOf _ * @since 0.8.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a plain object, else `false`. * @example * * function Foo() { * this.a = 1; * } * * _.isPlainObject(new Foo); * // => false * * _.isPlainObject([1, 2, 3]); * // => false * * _.isPlainObject({ 'x': 0, 'y': 0 }); * // => true * * _.isPlainObject(Object.create(null)); * // => true */ function isPlainObject(value) { if (!isObjectLike(value) || baseGetTag(value) != objectTag) { return false; } var proto = getPrototype(value); if (proto === null) { return true; } var Ctor = hasOwnProperty.call(proto, 'constructor') && proto.constructor; return typeof Ctor == 'function' && Ctor instanceof Ctor && funcToString.call(Ctor) == objectCtorString; } /** * Checks if `value` is classified as a `RegExp` object. * * @static * @memberOf _ * @since 0.1.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a regexp, else `false`. * @example * * _.isRegExp(/abc/); * // => true * * _.isRegExp('/abc/'); * // => false */ var isRegExp = nodeIsRegExp ? baseUnary(nodeIsRegExp) : baseIsRegExp; /** * Checks if `value` is a safe integer. An integer is safe if it's an IEEE-754 * double precision number which isn't the result of a rounded unsafe integer. * * **Note:** This method is based on * [`Number.isSafeInteger`](https://mdn.io/Number/isSafeInteger). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a safe integer, else `false`. * @example * * _.isSafeInteger(3); * // => true * * _.isSafeInteger(Number.MIN_VALUE); * // => false * * _.isSafeInteger(Infinity); * // => false * * _.isSafeInteger('3'); * // => false */ function isSafeInteger(value) { return isInteger(value) && value >= -MAX_SAFE_INTEGER && value <= MAX_SAFE_INTEGER; } /** * Checks if `value` is classified as a `Set` object. * * @static * @memberOf _ * @since 4.3.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a set, else `false`. * @example * * _.isSet(new Set); * // => true * * _.isSet(new WeakSet); * // => false */ var isSet = nodeIsSet ? baseUnary(nodeIsSet) : baseIsSet; /** * Checks if `value` is classified as a `String` primitive or object. * * @static * @since 0.1.0 * @memberOf _ * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a string, else `false`. * @example * * _.isString('abc'); * // => true * * _.isString(1); * // => false */ function isString(value) { return typeof value == 'string' || (!isArray(value) && isObjectLike(value) && baseGetTag(value) == stringTag); } /** * Checks if `value` is classified as a `Symbol` primitive or object. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a symbol, else `false`. * @example * * _.isSymbol(Symbol.iterator); * // => true * * _.isSymbol('abc'); * // => false */ function isSymbol(value) { return typeof value == 'symbol' || (isObjectLike(value) && baseGetTag(value) == symbolTag); } /** * Checks if `value` is classified as a typed array. * * @static * @memberOf _ * @since 3.0.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a typed array, else `false`. * @example * * _.isTypedArray(new Uint8Array); * // => true * * _.isTypedArray([]); * // => false */ var isTypedArray = nodeIsTypedArray ? baseUnary(nodeIsTypedArray) : baseIsTypedArray; /** * Checks if `value` is `undefined`. * * @static * @since 0.1.0 * @memberOf _ * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is `undefined`, else `false`. * @example * * _.isUndefined(void 0); * // => true * * _.isUndefined(null); * // => false */ function isUndefined(value) { return value === undefined; } /** * Checks if `value` is classified as a `WeakMap` object. * * @static * @memberOf _ * @since 4.3.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a weak map, else `false`. * @example * * _.isWeakMap(new WeakMap); * // => true * * _.isWeakMap(new Map); * // => false */ function isWeakMap(value) { return isObjectLike(value) && getTag(value) == weakMapTag; } /** * Checks if `value` is classified as a `WeakSet` object. * * @static * @memberOf _ * @since 4.3.0 * @category Lang * @param {*} value The value to check. * @returns {boolean} Returns `true` if `value` is a weak set, else `false`. * @example * * _.isWeakSet(new WeakSet); * // => true * * _.isWeakSet(new Set); * // => false */ function isWeakSet(value) { return isObjectLike(value) && baseGetTag(value) == weakSetTag; } /** * Checks if `value` is less than `other`. * * @static * @memberOf _ * @since 3.9.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if `value` is less than `other`, * else `false`. * @see _.gt * @example * * _.lt(1, 3); * // => true * * _.lt(3, 3); * // => false * * _.lt(3, 1); * // => false */ var lt = createRelationalOperation(baseLt); /** * Checks if `value` is less than or equal to `other`. * * @static * @memberOf _ * @since 3.9.0 * @category Lang * @param {*} value The value to compare. * @param {*} other The other value to compare. * @returns {boolean} Returns `true` if `value` is less than or equal to * `other`, else `false`. * @see _.gte * @example * * _.lte(1, 3); * // => true * * _.lte(3, 3); * // => true * * _.lte(3, 1); * // => false */ var lte = createRelationalOperation(function(value, other) { return value <= other; }); /** * Converts `value` to an array. * * @static * @since 0.1.0 * @memberOf _ * @category Lang * @param {*} value The value to convert. * @returns {Array} Returns the converted array. * @example * * _.toArray({ 'a': 1, 'b': 2 }); * // => [1, 2] * * _.toArray('abc'); * // => ['a', 'b', 'c'] * * _.toArray(1); * // => [] * * _.toArray(null); * // => [] */ function toArray(value) { if (!value) { return []; } if (isArrayLike(value)) { return isString(value) ? stringToArray(value) : copyArray(value); } if (symIterator && value[symIterator]) { return iteratorToArray(value[symIterator]()); } var tag = getTag(value), func = tag == mapTag ? mapToArray : (tag == setTag ? setToArray : values); return func(value); } /** * Converts `value` to a finite number. * * @static * @memberOf _ * @since 4.12.0 * @category Lang * @param {*} value The value to convert. * @returns {number} Returns the converted number. * @example * * _.toFinite(3.2); * // => 3.2 * * _.toFinite(Number.MIN_VALUE); * // => 5e-324 * * _.toFinite(Infinity); * // => 1.7976931348623157e+308 * * _.toFinite('3.2'); * // => 3.2 */ function toFinite(value) { if (!value) { return value === 0 ? value : 0; } value = toNumber(value); if (value === INFINITY || value === -INFINITY) { var sign = (value < 0 ? -1 : 1); return sign * MAX_INTEGER; } return value === value ? value : 0; } /** * Converts `value` to an integer. * * **Note:** This method is loosely based on * [`ToInteger`](http://www.ecma-international.org/ecma-262/7.0/#sec-tointeger). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to convert. * @returns {number} Returns the converted integer. * @example * * _.toInteger(3.2); * // => 3 * * _.toInteger(Number.MIN_VALUE); * // => 0 * * _.toInteger(Infinity); * // => 1.7976931348623157e+308 * * _.toInteger('3.2'); * // => 3 */ function toInteger(value) { var result = toFinite(value), remainder = result % 1; return result === result ? (remainder ? result - remainder : result) : 0; } /** * Converts `value` to an integer suitable for use as the length of an * array-like object. * * **Note:** This method is based on * [`ToLength`](http://ecma-international.org/ecma-262/7.0/#sec-tolength). * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to convert. * @returns {number} Returns the converted integer. * @example * * _.toLength(3.2); * // => 3 * * _.toLength(Number.MIN_VALUE); * // => 0 * * _.toLength(Infinity); * // => 4294967295 * * _.toLength('3.2'); * // => 3 */ function toLength(value) { return value ? baseClamp(toInteger(value), 0, MAX_ARRAY_LENGTH) : 0; } /** * Converts `value` to a number. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to process. * @returns {number} Returns the number. * @example * * _.toNumber(3.2); * // => 3.2 * * _.toNumber(Number.MIN_VALUE); * // => 5e-324 * * _.toNumber(Infinity); * // => Infinity * * _.toNumber('3.2'); * // => 3.2 */ function toNumber(value) { if (typeof value == 'number') { return value; } if (isSymbol(value)) { return NAN; } if (isObject(value)) { var other = typeof value.valueOf == 'function' ? value.valueOf() : value; value = isObject(other) ? (other + '') : other; } if (typeof value != 'string') { return value === 0 ? value : +value; } value = value.replace(reTrim, ''); var isBinary = reIsBinary.test(value); return (isBinary || reIsOctal.test(value)) ? freeParseInt(value.slice(2), isBinary ? 2 : 8) : (reIsBadHex.test(value) ? NAN : +value); } /** * Converts `value` to a plain object flattening inherited enumerable string * keyed properties of `value` to own properties of the plain object. * * @static * @memberOf _ * @since 3.0.0 * @category Lang * @param {*} value The value to convert. * @returns {Object} Returns the converted plain object. * @example * * function Foo() { * this.b = 2; * } * * Foo.prototype.c = 3; * * _.assign({ 'a': 1 }, new Foo); * // => { 'a': 1, 'b': 2 } * * _.assign({ 'a': 1 }, _.toPlainObject(new Foo)); * // => { 'a': 1, 'b': 2, 'c': 3 } */ function toPlainObject(value) { return copyObject(value, keysIn(value)); } /** * Converts `value` to a safe integer. A safe integer can be compared and * represented correctly. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to convert. * @returns {number} Returns the converted integer. * @example * * _.toSafeInteger(3.2); * // => 3 * * _.toSafeInteger(Number.MIN_VALUE); * // => 0 * * _.toSafeInteger(Infinity); * // => 9007199254740991 * * _.toSafeInteger('3.2'); * // => 3 */ function toSafeInteger(value) { return value ? baseClamp(toInteger(value), -MAX_SAFE_INTEGER, MAX_SAFE_INTEGER) : (value === 0 ? value : 0); } /** * Converts `value` to a string. An empty string is returned for `null` * and `undefined` values. The sign of `-0` is preserved. * * @static * @memberOf _ * @since 4.0.0 * @category Lang * @param {*} value The value to convert. * @returns {string} Returns the converted string. * @example * * _.toString(null); * // => '' * * _.toString(-0); * // => '-0' * * _.toString([1, 2, 3]); * // => '1,2,3' */ function toString(value) { return value == null ? '' : baseToString(value); } /*------------------------------------------------------------------------*/ /** * Assigns own enumerable string keyed properties of source objects to the * destination object. Source objects are applied from left to right. * Subsequent sources overwrite property assignments of previous sources. * * **Note:** This method mutates `object` and is loosely based on * [`Object.assign`](https://mdn.io/Object/assign). * * @static * @memberOf _ * @since 0.10.0 * @category Object * @param {Object} object The destination object. * @param {...Object} [sources] The source objects. * @returns {Object} Returns `object`. * @see _.assignIn * @example * * function Foo() { * this.a = 1; * } * * function Bar() { * this.c = 3; * } * * Foo.prototype.b = 2; * Bar.prototype.d = 4; * * _.assign({ 'a': 0 }, new Foo, new Bar); * // => { 'a': 1, 'c': 3 } */ var assign = createAssigner(function(object, source) { if (isPrototype(source) || isArrayLike(source)) { copyObject(source, keys(source), object); return; } for (var key in source) { if (hasOwnProperty.call(source, key)) { assignValue(object, key, source[key]); } } }); /** * This method is like `_.assign` except that it iterates over own and * inherited source properties. * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.0.0 * @alias extend * @category Object * @param {Object} object The destination object. * @param {...Object} [sources] The source objects. * @returns {Object} Returns `object`. * @see _.assign * @example * * function Foo() { * this.a = 1; * } * * function Bar() { * this.c = 3; * } * * Foo.prototype.b = 2; * Bar.prototype.d = 4; * * _.assignIn({ 'a': 0 }, new Foo, new Bar); * // => { 'a': 1, 'b': 2, 'c': 3, 'd': 4 } */ var assignIn = createAssigner(function(object, source) { copyObject(source, keysIn(source), object); }); /** * This method is like `_.assignIn` except that it accepts `customizer` * which is invoked to produce the assigned values. If `customizer` returns * `undefined`, assignment is handled by the method instead. The `customizer` * is invoked with five arguments: (objValue, srcValue, key, object, source). * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.0.0 * @alias extendWith * @category Object * @param {Object} object The destination object. * @param {...Object} sources The source objects. * @param {Function} [customizer] The function to customize assigned values. * @returns {Object} Returns `object`. * @see _.assignWith * @example * * function customizer(objValue, srcValue) { * return _.isUndefined(objValue) ? srcValue : objValue; * } * * var defaults = _.partialRight(_.assignInWith, customizer); * * defaults({ 'a': 1 }, { 'b': 2 }, { 'a': 3 }); * // => { 'a': 1, 'b': 2 } */ var assignInWith = createAssigner(function(object, source, srcIndex, customizer) { copyObject(source, keysIn(source), object, customizer); }); /** * This method is like `_.assign` except that it accepts `customizer` * which is invoked to produce the assigned values. If `customizer` returns * `undefined`, assignment is handled by the method instead. The `customizer` * is invoked with five arguments: (objValue, srcValue, key, object, source). * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The destination object. * @param {...Object} sources The source objects. * @param {Function} [customizer] The function to customize assigned values. * @returns {Object} Returns `object`. * @see _.assignInWith * @example * * function customizer(objValue, srcValue) { * return _.isUndefined(objValue) ? srcValue : objValue; * } * * var defaults = _.partialRight(_.assignWith, customizer); * * defaults({ 'a': 1 }, { 'b': 2 }, { 'a': 3 }); * // => { 'a': 1, 'b': 2 } */ var assignWith = createAssigner(function(object, source, srcIndex, customizer) { copyObject(source, keys(source), object, customizer); }); /** * Creates an array of values corresponding to `paths` of `object`. * * @static * @memberOf _ * @since 1.0.0 * @category Object * @param {Object} object The object to iterate over. * @param {...(string|string[])} [paths] The property paths to pick. * @returns {Array} Returns the picked values. * @example * * var object = { 'a': [{ 'b': { 'c': 3 } }, 4] }; * * _.at(object, ['a[0].b.c', 'a[1]']); * // => [3, 4] */ var at = flatRest(baseAt); /** * Creates an object that inherits from the `prototype` object. If a * `properties` object is given, its own enumerable string keyed properties * are assigned to the created object. * * @static * @memberOf _ * @since 2.3.0 * @category Object * @param {Object} prototype The object to inherit from. * @param {Object} [properties] The properties to assign to the object. * @returns {Object} Returns the new object. * @example * * function Shape() { * this.x = 0; * this.y = 0; * } * * function Circle() { * Shape.call(this); * } * * Circle.prototype = _.create(Shape.prototype, { * 'constructor': Circle * }); * * var circle = new Circle; * circle instanceof Circle; * // => true * * circle instanceof Shape; * // => true */ function create(prototype, properties) { var result = baseCreate(prototype); return properties == null ? result : baseAssign(result, properties); } /** * Assigns own and inherited enumerable string keyed properties of source * objects to the destination object for all destination properties that * resolve to `undefined`. Source objects are applied from left to right. * Once a property is set, additional values of the same property are ignored. * * **Note:** This method mutates `object`. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The destination object. * @param {...Object} [sources] The source objects. * @returns {Object} Returns `object`. * @see _.defaultsDeep * @example * * _.defaults({ 'a': 1 }, { 'b': 2 }, { 'a': 3 }); * // => { 'a': 1, 'b': 2 } */ var defaults = baseRest(function(args) { args.push(undefined, customDefaultsAssignIn); return apply(assignInWith, undefined, args); }); /** * This method is like `_.defaults` except that it recursively assigns * default properties. * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 3.10.0 * @category Object * @param {Object} object The destination object. * @param {...Object} [sources] The source objects. * @returns {Object} Returns `object`. * @see _.defaults * @example * * _.defaultsDeep({ 'a': { 'b': 2 } }, { 'a': { 'b': 1, 'c': 3 } }); * // => { 'a': { 'b': 2, 'c': 3 } } */ var defaultsDeep = baseRest(function(args) { args.push(undefined, customDefaultsMerge); return apply(mergeWith, undefined, args); }); /** * This method is like `_.find` except that it returns the key of the first * element `predicate` returns truthy for instead of the element itself. * * @static * @memberOf _ * @since 1.1.0 * @category Object * @param {Object} object The object to inspect. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {string|undefined} Returns the key of the matched element, * else `undefined`. * @example * * var users = { * 'barney': { 'age': 36, 'active': true }, * 'fred': { 'age': 40, 'active': false }, * 'pebbles': { 'age': 1, 'active': true } * }; * * _.findKey(users, function(o) { return o.age < 40; }); * // => 'barney' (iteration order is not guaranteed) * * // The `_.matches` iteratee shorthand. * _.findKey(users, { 'age': 1, 'active': true }); * // => 'pebbles' * * // The `_.matchesProperty` iteratee shorthand. * _.findKey(users, ['active', false]); * // => 'fred' * * // The `_.property` iteratee shorthand. * _.findKey(users, 'active'); * // => 'barney' */ function findKey(object, predicate) { return baseFindKey(object, getIteratee(predicate, 3), baseForOwn); } /** * This method is like `_.findKey` except that it iterates over elements of * a collection in the opposite order. * * @static * @memberOf _ * @since 2.0.0 * @category Object * @param {Object} object The object to inspect. * @param {Function} [predicate=_.identity] The function invoked per iteration. * @returns {string|undefined} Returns the key of the matched element, * else `undefined`. * @example * * var users = { * 'barney': { 'age': 36, 'active': true }, * 'fred': { 'age': 40, 'active': false }, * 'pebbles': { 'age': 1, 'active': true } * }; * * _.findLastKey(users, function(o) { return o.age < 40; }); * // => returns 'pebbles' assuming `_.findKey` returns 'barney' * * // The `_.matches` iteratee shorthand. * _.findLastKey(users, { 'age': 36, 'active': true }); * // => 'barney' * * // The `_.matchesProperty` iteratee shorthand. * _.findLastKey(users, ['active', false]); * // => 'fred' * * // The `_.property` iteratee shorthand. * _.findLastKey(users, 'active'); * // => 'pebbles' */ function findLastKey(object, predicate) { return baseFindKey(object, getIteratee(predicate, 3), baseForOwnRight); } /** * Iterates over own and inherited enumerable string keyed properties of an * object and invokes `iteratee` for each property. The iteratee is invoked * with three arguments: (value, key, object). Iteratee functions may exit * iteration early by explicitly returning `false`. * * @static * @memberOf _ * @since 0.3.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Object} Returns `object`. * @see _.forInRight * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.forIn(new Foo, function(value, key) { * console.log(key); * }); * // => Logs 'a', 'b', then 'c' (iteration order is not guaranteed). */ function forIn(object, iteratee) { return object == null ? object : baseFor(object, getIteratee(iteratee, 3), keysIn); } /** * This method is like `_.forIn` except that it iterates over properties of * `object` in the opposite order. * * @static * @memberOf _ * @since 2.0.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Object} Returns `object`. * @see _.forIn * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.forInRight(new Foo, function(value, key) { * console.log(key); * }); * // => Logs 'c', 'b', then 'a' assuming `_.forIn` logs 'a', 'b', then 'c'. */ function forInRight(object, iteratee) { return object == null ? object : baseForRight(object, getIteratee(iteratee, 3), keysIn); } /** * Iterates over own enumerable string keyed properties of an object and * invokes `iteratee` for each property. The iteratee is invoked with three * arguments: (value, key, object). Iteratee functions may exit iteration * early by explicitly returning `false`. * * @static * @memberOf _ * @since 0.3.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Object} Returns `object`. * @see _.forOwnRight * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.forOwn(new Foo, function(value, key) { * console.log(key); * }); * // => Logs 'a' then 'b' (iteration order is not guaranteed). */ function forOwn(object, iteratee) { return object && baseForOwn(object, getIteratee(iteratee, 3)); } /** * This method is like `_.forOwn` except that it iterates over properties of * `object` in the opposite order. * * @static * @memberOf _ * @since 2.0.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Object} Returns `object`. * @see _.forOwn * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.forOwnRight(new Foo, function(value, key) { * console.log(key); * }); * // => Logs 'b' then 'a' assuming `_.forOwn` logs 'a' then 'b'. */ function forOwnRight(object, iteratee) { return object && baseForOwnRight(object, getIteratee(iteratee, 3)); } /** * Creates an array of function property names from own enumerable properties * of `object`. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The object to inspect. * @returns {Array} Returns the function names. * @see _.functionsIn * @example * * function Foo() { * this.a = _.constant('a'); * this.b = _.constant('b'); * } * * Foo.prototype.c = _.constant('c'); * * _.functions(new Foo); * // => ['a', 'b'] */ function functions(object) { return object == null ? [] : baseFunctions(object, keys(object)); } /** * Creates an array of function property names from own and inherited * enumerable properties of `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The object to inspect. * @returns {Array} Returns the function names. * @see _.functions * @example * * function Foo() { * this.a = _.constant('a'); * this.b = _.constant('b'); * } * * Foo.prototype.c = _.constant('c'); * * _.functionsIn(new Foo); * // => ['a', 'b', 'c'] */ function functionsIn(object) { return object == null ? [] : baseFunctions(object, keysIn(object)); } /** * Gets the value at `path` of `object`. If the resolved value is * `undefined`, the `defaultValue` is returned in its place. * * @static * @memberOf _ * @since 3.7.0 * @category Object * @param {Object} object The object to query. * @param {Array|string} path The path of the property to get. * @param {*} [defaultValue] The value returned for `undefined` resolved values. * @returns {*} Returns the resolved value. * @example * * var object = { 'a': [{ 'b': { 'c': 3 } }] }; * * _.get(object, 'a[0].b.c'); * // => 3 * * _.get(object, ['a', '0', 'b', 'c']); * // => 3 * * _.get(object, 'a.b.c', 'default'); * // => 'default' */ function get(object, path, defaultValue) { var result = object == null ? undefined : baseGet(object, path); return result === undefined ? defaultValue : result; } /** * Checks if `path` is a direct property of `object`. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The object to query. * @param {Array|string} path The path to check. * @returns {boolean} Returns `true` if `path` exists, else `false`. * @example * * var object = { 'a': { 'b': 2 } }; * var other = _.create({ 'a': _.create({ 'b': 2 }) }); * * _.has(object, 'a'); * // => true * * _.has(object, 'a.b'); * // => true * * _.has(object, ['a', 'b']); * // => true * * _.has(other, 'a'); * // => false */ function has(object, path) { return object != null && hasPath(object, path, baseHas); } /** * Checks if `path` is a direct or inherited property of `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The object to query. * @param {Array|string} path The path to check. * @returns {boolean} Returns `true` if `path` exists, else `false`. * @example * * var object = _.create({ 'a': _.create({ 'b': 2 }) }); * * _.hasIn(object, 'a'); * // => true * * _.hasIn(object, 'a.b'); * // => true * * _.hasIn(object, ['a', 'b']); * // => true * * _.hasIn(object, 'b'); * // => false */ function hasIn(object, path) { return object != null && hasPath(object, path, baseHasIn); } /** * Creates an object composed of the inverted keys and values of `object`. * If `object` contains duplicate values, subsequent values overwrite * property assignments of previous values. * * @static * @memberOf _ * @since 0.7.0 * @category Object * @param {Object} object The object to invert. * @returns {Object} Returns the new inverted object. * @example * * var object = { 'a': 1, 'b': 2, 'c': 1 }; * * _.invert(object); * // => { '1': 'c', '2': 'b' } */ var invert = createInverter(function(result, value, key) { result[value] = key; }, constant(identity)); /** * This method is like `_.invert` except that the inverted object is generated * from the results of running each element of `object` thru `iteratee`. The * corresponding inverted value of each inverted key is an array of keys * responsible for generating the inverted value. The iteratee is invoked * with one argument: (value). * * @static * @memberOf _ * @since 4.1.0 * @category Object * @param {Object} object The object to invert. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {Object} Returns the new inverted object. * @example * * var object = { 'a': 1, 'b': 2, 'c': 1 }; * * _.invertBy(object); * // => { '1': ['a', 'c'], '2': ['b'] } * * _.invertBy(object, function(value) { * return 'group' + value; * }); * // => { 'group1': ['a', 'c'], 'group2': ['b'] } */ var invertBy = createInverter(function(result, value, key) { if (hasOwnProperty.call(result, value)) { result[value].push(key); } else { result[value] = [key]; } }, getIteratee); /** * Invokes the method at `path` of `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The object to query. * @param {Array|string} path The path of the method to invoke. * @param {...*} [args] The arguments to invoke the method with. * @returns {*} Returns the result of the invoked method. * @example * * var object = { 'a': [{ 'b': { 'c': [1, 2, 3, 4] } }] }; * * _.invoke(object, 'a[0].b.c.slice', 1, 3); * // => [2, 3] */ var invoke = baseRest(baseInvoke); /** * Creates an array of the own enumerable property names of `object`. * * **Note:** Non-object values are coerced to objects. See the * [ES spec](http://ecma-international.org/ecma-262/7.0/#sec-object.keys) * for more details. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The object to query. * @returns {Array} Returns the array of property names. * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.keys(new Foo); * // => ['a', 'b'] (iteration order is not guaranteed) * * _.keys('hi'); * // => ['0', '1'] */ function keys(object) { return isArrayLike(object) ? arrayLikeKeys(object) : baseKeys(object); } /** * Creates an array of the own and inherited enumerable property names of `object`. * * **Note:** Non-object values are coerced to objects. * * @static * @memberOf _ * @since 3.0.0 * @category Object * @param {Object} object The object to query. * @returns {Array} Returns the array of property names. * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.keysIn(new Foo); * // => ['a', 'b', 'c'] (iteration order is not guaranteed) */ function keysIn(object) { return isArrayLike(object) ? arrayLikeKeys(object, true) : baseKeysIn(object); } /** * The opposite of `_.mapValues`; this method creates an object with the * same values as `object` and keys generated by running each own enumerable * string keyed property of `object` thru `iteratee`. The iteratee is invoked * with three arguments: (value, key, object). * * @static * @memberOf _ * @since 3.8.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Object} Returns the new mapped object. * @see _.mapValues * @example * * _.mapKeys({ 'a': 1, 'b': 2 }, function(value, key) { * return key + value; * }); * // => { 'a1': 1, 'b2': 2 } */ function mapKeys(object, iteratee) { var result = {}; iteratee = getIteratee(iteratee, 3); baseForOwn(object, function(value, key, object) { baseAssignValue(result, iteratee(value, key, object), value); }); return result; } /** * Creates an object with the same keys as `object` and values generated * by running each own enumerable string keyed property of `object` thru * `iteratee`. The iteratee is invoked with three arguments: * (value, key, object). * * @static * @memberOf _ * @since 2.4.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Object} Returns the new mapped object. * @see _.mapKeys * @example * * var users = { * 'fred': { 'user': 'fred', 'age': 40 }, * 'pebbles': { 'user': 'pebbles', 'age': 1 } * }; * * _.mapValues(users, function(o) { return o.age; }); * // => { 'fred': 40, 'pebbles': 1 } (iteration order is not guaranteed) * * // The `_.property` iteratee shorthand. * _.mapValues(users, 'age'); * // => { 'fred': 40, 'pebbles': 1 } (iteration order is not guaranteed) */ function mapValues(object, iteratee) { var result = {}; iteratee = getIteratee(iteratee, 3); baseForOwn(object, function(value, key, object) { baseAssignValue(result, key, iteratee(value, key, object)); }); return result; } /** * This method is like `_.assign` except that it recursively merges own and * inherited enumerable string keyed properties of source objects into the * destination object. Source properties that resolve to `undefined` are * skipped if a destination value exists. Array and plain object properties * are merged recursively. Other objects and value types are overridden by * assignment. Source objects are applied from left to right. Subsequent * sources overwrite property assignments of previous sources. * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 0.5.0 * @category Object * @param {Object} object The destination object. * @param {...Object} [sources] The source objects. * @returns {Object} Returns `object`. * @example * * var object = { * 'a': [{ 'b': 2 }, { 'd': 4 }] * }; * * var other = { * 'a': [{ 'c': 3 }, { 'e': 5 }] * }; * * _.merge(object, other); * // => { 'a': [{ 'b': 2, 'c': 3 }, { 'd': 4, 'e': 5 }] } */ var merge = createAssigner(function(object, source, srcIndex) { baseMerge(object, source, srcIndex); }); /** * This method is like `_.merge` except that it accepts `customizer` which * is invoked to produce the merged values of the destination and source * properties. If `customizer` returns `undefined`, merging is handled by the * method instead. The `customizer` is invoked with six arguments: * (objValue, srcValue, key, object, source, stack). * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The destination object. * @param {...Object} sources The source objects. * @param {Function} customizer The function to customize assigned values. * @returns {Object} Returns `object`. * @example * * function customizer(objValue, srcValue) { * if (_.isArray(objValue)) { * return objValue.concat(srcValue); * } * } * * var object = { 'a': [1], 'b': [2] }; * var other = { 'a': [3], 'b': [4] }; * * _.mergeWith(object, other, customizer); * // => { 'a': [1, 3], 'b': [2, 4] } */ var mergeWith = createAssigner(function(object, source, srcIndex, customizer) { baseMerge(object, source, srcIndex, customizer); }); /** * The opposite of `_.pick`; this method creates an object composed of the * own and inherited enumerable property paths of `object` that are not omitted. * * **Note:** This method is considerably slower than `_.pick`. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The source object. * @param {...(string|string[])} [paths] The property paths to omit. * @returns {Object} Returns the new object. * @example * * var object = { 'a': 1, 'b': '2', 'c': 3 }; * * _.omit(object, ['a', 'c']); * // => { 'b': '2' } */ var omit = flatRest(function(object, paths) { var result = {}; if (object == null) { return result; } var isDeep = false; paths = arrayMap(paths, function(path) { path = castPath(path, object); isDeep || (isDeep = path.length > 1); return path; }); copyObject(object, getAllKeysIn(object), result); if (isDeep) { result = baseClone(result, CLONE_DEEP_FLAG | CLONE_FLAT_FLAG | CLONE_SYMBOLS_FLAG, customOmitClone); } var length = paths.length; while (length--) { baseUnset(result, paths[length]); } return result; }); /** * The opposite of `_.pickBy`; this method creates an object composed of * the own and inherited enumerable string keyed properties of `object` that * `predicate` doesn't return truthy for. The predicate is invoked with two * arguments: (value, key). * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The source object. * @param {Function} [predicate=_.identity] The function invoked per property. * @returns {Object} Returns the new object. * @example * * var object = { 'a': 1, 'b': '2', 'c': 3 }; * * _.omitBy(object, _.isNumber); * // => { 'b': '2' } */ function omitBy(object, predicate) { return pickBy(object, negate(getIteratee(predicate))); } /** * Creates an object composed of the picked `object` properties. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The source object. * @param {...(string|string[])} [paths] The property paths to pick. * @returns {Object} Returns the new object. * @example * * var object = { 'a': 1, 'b': '2', 'c': 3 }; * * _.pick(object, ['a', 'c']); * // => { 'a': 1, 'c': 3 } */ var pick = flatRest(function(object, paths) { return object == null ? {} : basePick(object, paths); }); /** * Creates an object composed of the `object` properties `predicate` returns * truthy for. The predicate is invoked with two arguments: (value, key). * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The source object. * @param {Function} [predicate=_.identity] The function invoked per property. * @returns {Object} Returns the new object. * @example * * var object = { 'a': 1, 'b': '2', 'c': 3 }; * * _.pickBy(object, _.isNumber); * // => { 'a': 1, 'c': 3 } */ function pickBy(object, predicate) { if (object == null) { return {}; } var props = arrayMap(getAllKeysIn(object), function(prop) { return [prop]; }); predicate = getIteratee(predicate); return basePickBy(object, props, function(value, path) { return predicate(value, path[0]); }); } /** * This method is like `_.get` except that if the resolved value is a * function it's invoked with the `this` binding of its parent object and * its result is returned. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The object to query. * @param {Array|string} path The path of the property to resolve. * @param {*} [defaultValue] The value returned for `undefined` resolved values. * @returns {*} Returns the resolved value. * @example * * var object = { 'a': [{ 'b': { 'c1': 3, 'c2': _.constant(4) } }] }; * * _.result(object, 'a[0].b.c1'); * // => 3 * * _.result(object, 'a[0].b.c2'); * // => 4 * * _.result(object, 'a[0].b.c3', 'default'); * // => 'default' * * _.result(object, 'a[0].b.c3', _.constant('default')); * // => 'default' */ function result(object, path, defaultValue) { path = castPath(path, object); var index = -1, length = path.length; // Ensure the loop is entered when path is empty. if (!length) { length = 1; object = undefined; } while (++index < length) { var value = object == null ? undefined : object[toKey(path[index])]; if (value === undefined) { index = length; value = defaultValue; } object = isFunction(value) ? value.call(object) : value; } return object; } /** * Sets the value at `path` of `object`. If a portion of `path` doesn't exist, * it's created. Arrays are created for missing index properties while objects * are created for all other missing properties. Use `_.setWith` to customize * `path` creation. * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 3.7.0 * @category Object * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to set. * @param {*} value The value to set. * @returns {Object} Returns `object`. * @example * * var object = { 'a': [{ 'b': { 'c': 3 } }] }; * * _.set(object, 'a[0].b.c', 4); * console.log(object.a[0].b.c); * // => 4 * * _.set(object, ['x', '0', 'y', 'z'], 5); * console.log(object.x[0].y.z); * // => 5 */ function set(object, path, value) { return object == null ? object : baseSet(object, path, value); } /** * This method is like `_.set` except that it accepts `customizer` which is * invoked to produce the objects of `path`. If `customizer` returns `undefined` * path creation is handled by the method instead. The `customizer` is invoked * with three arguments: (nsValue, key, nsObject). * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to set. * @param {*} value The value to set. * @param {Function} [customizer] The function to customize assigned values. * @returns {Object} Returns `object`. * @example * * var object = {}; * * _.setWith(object, '[0][1]', 'a', Object); * // => { '0': { '1': 'a' } } */ function setWith(object, path, value, customizer) { customizer = typeof customizer == 'function' ? customizer : undefined; return object == null ? object : baseSet(object, path, value, customizer); } /** * Creates an array of own enumerable string keyed-value pairs for `object` * which can be consumed by `_.fromPairs`. If `object` is a map or set, its * entries are returned. * * @static * @memberOf _ * @since 4.0.0 * @alias entries * @category Object * @param {Object} object The object to query. * @returns {Array} Returns the key-value pairs. * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.toPairs(new Foo); * // => [['a', 1], ['b', 2]] (iteration order is not guaranteed) */ var toPairs = createToPairs(keys); /** * Creates an array of own and inherited enumerable string keyed-value pairs * for `object` which can be consumed by `_.fromPairs`. If `object` is a map * or set, its entries are returned. * * @static * @memberOf _ * @since 4.0.0 * @alias entriesIn * @category Object * @param {Object} object The object to query. * @returns {Array} Returns the key-value pairs. * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.toPairsIn(new Foo); * // => [['a', 1], ['b', 2], ['c', 3]] (iteration order is not guaranteed) */ var toPairsIn = createToPairs(keysIn); /** * An alternative to `_.reduce`; this method transforms `object` to a new * `accumulator` object which is the result of running each of its own * enumerable string keyed properties thru `iteratee`, with each invocation * potentially mutating the `accumulator` object. If `accumulator` is not * provided, a new object with the same `[[Prototype]]` will be used. The * iteratee is invoked with four arguments: (accumulator, value, key, object). * Iteratee functions may exit iteration early by explicitly returning `false`. * * @static * @memberOf _ * @since 1.3.0 * @category Object * @param {Object} object The object to iterate over. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @param {*} [accumulator] The custom accumulator value. * @returns {*} Returns the accumulated value. * @example * * _.transform([2, 3, 4], function(result, n) { * result.push(n *= n); * return n % 2 == 0; * }, []); * // => [4, 9] * * _.transform({ 'a': 1, 'b': 2, 'c': 1 }, function(result, value, key) { * (result[value] || (result[value] = [])).push(key); * }, {}); * // => { '1': ['a', 'c'], '2': ['b'] } */ function transform(object, iteratee, accumulator) { var isArr = isArray(object), isArrLike = isArr || isBuffer(object) || isTypedArray(object); iteratee = getIteratee(iteratee, 4); if (accumulator == null) { var Ctor = object && object.constructor; if (isArrLike) { accumulator = isArr ? new Ctor : []; } else if (isObject(object)) { accumulator = isFunction(Ctor) ? baseCreate(getPrototype(object)) : {}; } else { accumulator = {}; } } (isArrLike ? arrayEach : baseForOwn)(object, function(value, index, object) { return iteratee(accumulator, value, index, object); }); return accumulator; } /** * Removes the property at `path` of `object`. * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.0.0 * @category Object * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to unset. * @returns {boolean} Returns `true` if the property is deleted, else `false`. * @example * * var object = { 'a': [{ 'b': { 'c': 7 } }] }; * _.unset(object, 'a[0].b.c'); * // => true * * console.log(object); * // => { 'a': [{ 'b': {} }] }; * * _.unset(object, ['a', '0', 'b', 'c']); * // => true * * console.log(object); * // => { 'a': [{ 'b': {} }] }; */ function unset(object, path) { return object == null ? true : baseUnset(object, path); } /** * This method is like `_.set` except that accepts `updater` to produce the * value to set. Use `_.updateWith` to customize `path` creation. The `updater` * is invoked with one argument: (value). * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.6.0 * @category Object * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to set. * @param {Function} updater The function to produce the updated value. * @returns {Object} Returns `object`. * @example * * var object = { 'a': [{ 'b': { 'c': 3 } }] }; * * _.update(object, 'a[0].b.c', function(n) { return n * n; }); * console.log(object.a[0].b.c); * // => 9 * * _.update(object, 'x[0].y.z', function(n) { return n ? n + 1 : 0; }); * console.log(object.x[0].y.z); * // => 0 */ function update(object, path, updater) { return object == null ? object : baseUpdate(object, path, castFunction(updater)); } /** * This method is like `_.update` except that it accepts `customizer` which is * invoked to produce the objects of `path`. If `customizer` returns `undefined` * path creation is handled by the method instead. The `customizer` is invoked * with three arguments: (nsValue, key, nsObject). * * **Note:** This method mutates `object`. * * @static * @memberOf _ * @since 4.6.0 * @category Object * @param {Object} object The object to modify. * @param {Array|string} path The path of the property to set. * @param {Function} updater The function to produce the updated value. * @param {Function} [customizer] The function to customize assigned values. * @returns {Object} Returns `object`. * @example * * var object = {}; * * _.updateWith(object, '[0][1]', _.constant('a'), Object); * // => { '0': { '1': 'a' } } */ function updateWith(object, path, updater, customizer) { customizer = typeof customizer == 'function' ? customizer : undefined; return object == null ? object : baseUpdate(object, path, castFunction(updater), customizer); } /** * Creates an array of the own enumerable string keyed property values of `object`. * * **Note:** Non-object values are coerced to objects. * * @static * @since 0.1.0 * @memberOf _ * @category Object * @param {Object} object The object to query. * @returns {Array} Returns the array of property values. * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.values(new Foo); * // => [1, 2] (iteration order is not guaranteed) * * _.values('hi'); * // => ['h', 'i'] */ function values(object) { return object == null ? [] : baseValues(object, keys(object)); } /** * Creates an array of the own and inherited enumerable string keyed property * values of `object`. * * **Note:** Non-object values are coerced to objects. * * @static * @memberOf _ * @since 3.0.0 * @category Object * @param {Object} object The object to query. * @returns {Array} Returns the array of property values. * @example * * function Foo() { * this.a = 1; * this.b = 2; * } * * Foo.prototype.c = 3; * * _.valuesIn(new Foo); * // => [1, 2, 3] (iteration order is not guaranteed) */ function valuesIn(object) { return object == null ? [] : baseValues(object, keysIn(object)); } /*------------------------------------------------------------------------*/ /** * Clamps `number` within the inclusive `lower` and `upper` bounds. * * @static * @memberOf _ * @since 4.0.0 * @category Number * @param {number} number The number to clamp. * @param {number} [lower] The lower bound. * @param {number} upper The upper bound. * @returns {number} Returns the clamped number. * @example * * _.clamp(-10, -5, 5); * // => -5 * * _.clamp(10, -5, 5); * // => 5 */ function clamp(number, lower, upper) { if (upper === undefined) { upper = lower; lower = undefined; } if (upper !== undefined) { upper = toNumber(upper); upper = upper === upper ? upper : 0; } if (lower !== undefined) { lower = toNumber(lower); lower = lower === lower ? lower : 0; } return baseClamp(toNumber(number), lower, upper); } /** * Checks if `n` is between `start` and up to, but not including, `end`. If * `end` is not specified, it's set to `start` with `start` then set to `0`. * If `start` is greater than `end` the params are swapped to support * negative ranges. * * @static * @memberOf _ * @since 3.3.0 * @category Number * @param {number} number The number to check. * @param {number} [start=0] The start of the range. * @param {number} end The end of the range. * @returns {boolean} Returns `true` if `number` is in the range, else `false`. * @see _.range, _.rangeRight * @example * * _.inRange(3, 2, 4); * // => true * * _.inRange(4, 8); * // => true * * _.inRange(4, 2); * // => false * * _.inRange(2, 2); * // => false * * _.inRange(1.2, 2); * // => true * * _.inRange(5.2, 4); * // => false * * _.inRange(-3, -2, -6); * // => true */ function inRange(number, start, end) { start = toFinite(start); if (end === undefined) { end = start; start = 0; } else { end = toFinite(end); } number = toNumber(number); return baseInRange(number, start, end); } /** * Produces a random number between the inclusive `lower` and `upper` bounds. * If only one argument is provided a number between `0` and the given number * is returned. If `floating` is `true`, or either `lower` or `upper` are * floats, a floating-point number is returned instead of an integer. * * **Note:** JavaScript follows the IEEE-754 standard for resolving * floating-point values which can produce unexpected results. * * @static * @memberOf _ * @since 0.7.0 * @category Number * @param {number} [lower=0] The lower bound. * @param {number} [upper=1] The upper bound. * @param {boolean} [floating] Specify returning a floating-point number. * @returns {number} Returns the random number. * @example * * _.random(0, 5); * // => an integer between 0 and 5 * * _.random(5); * // => also an integer between 0 and 5 * * _.random(5, true); * // => a floating-point number between 0 and 5 * * _.random(1.2, 5.2); * // => a floating-point number between 1.2 and 5.2 */ function random(lower, upper, floating) { if (floating && typeof floating != 'boolean' && isIterateeCall(lower, upper, floating)) { upper = floating = undefined; } if (floating === undefined) { if (typeof upper == 'boolean') { floating = upper; upper = undefined; } else if (typeof lower == 'boolean') { floating = lower; lower = undefined; } } if (lower === undefined && upper === undefined) { lower = 0; upper = 1; } else { lower = toFinite(lower); if (upper === undefined) { upper = lower; lower = 0; } else { upper = toFinite(upper); } } if (lower > upper) { var temp = lower; lower = upper; upper = temp; } if (floating || lower % 1 || upper % 1) { var rand = nativeRandom(); return nativeMin(lower + (rand * (upper - lower + freeParseFloat('1e-' + ((rand + '').length - 1)))), upper); } return baseRandom(lower, upper); } /*------------------------------------------------------------------------*/ /** * Converts `string` to [camel case](https://en.wikipedia.org/wiki/CamelCase). * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the camel cased string. * @example * * _.camelCase('Foo Bar'); * // => 'fooBar' * * _.camelCase('--foo-bar--'); * // => 'fooBar' * * _.camelCase('__FOO_BAR__'); * // => 'fooBar' */ var camelCase = createCompounder(function(result, word, index) { word = word.toLowerCase(); return result + (index ? capitalize(word) : word); }); /** * Converts the first character of `string` to upper case and the remaining * to lower case. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to capitalize. * @returns {string} Returns the capitalized string. * @example * * _.capitalize('FRED'); * // => 'Fred' */ function capitalize(string) { return upperFirst(toString(string).toLowerCase()); } /** * Deburrs `string` by converting * [Latin-1 Supplement](https://en.wikipedia.org/wiki/Latin-1_Supplement_(Unicode_block)#Character_table) * and [Latin Extended-A](https://en.wikipedia.org/wiki/Latin_Extended-A) * letters to basic Latin letters and removing * [combining diacritical marks](https://en.wikipedia.org/wiki/Combining_Diacritical_Marks). * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to deburr. * @returns {string} Returns the deburred string. * @example * * _.deburr('déjà vu'); * // => 'deja vu' */ function deburr(string) { string = toString(string); return string && string.replace(reLatin, deburrLetter).replace(reComboMark, ''); } /** * Checks if `string` ends with the given target string. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to inspect. * @param {string} [target] The string to search for. * @param {number} [position=string.length] The position to search up to. * @returns {boolean} Returns `true` if `string` ends with `target`, * else `false`. * @example * * _.endsWith('abc', 'c'); * // => true * * _.endsWith('abc', 'b'); * // => false * * _.endsWith('abc', 'b', 2); * // => true */ function endsWith(string, target, position) { string = toString(string); target = baseToString(target); var length = string.length; position = position === undefined ? length : baseClamp(toInteger(position), 0, length); var end = position; position -= target.length; return position >= 0 && string.slice(position, end) == target; } /** * Converts the characters "&", "<", ">", '"', and "'" in `string` to their * corresponding HTML entities. * * **Note:** No other characters are escaped. To escape additional * characters use a third-party library like [_he_](https://mths.be/he). * * Though the ">" character is escaped for symmetry, characters like * ">" and "/" don't need escaping in HTML and have no special meaning * unless they're part of a tag or unquoted attribute value. See * [Mathias Bynens's article](https://mathiasbynens.be/notes/ambiguous-ampersands) * (under "semi-related fun fact") for more details. * * When working with HTML you should always * [quote attribute values](http://wonko.com/post/html-escaping) to reduce * XSS vectors. * * @static * @since 0.1.0 * @memberOf _ * @category String * @param {string} [string=''] The string to escape. * @returns {string} Returns the escaped string. * @example * * _.escape('fred, barney, & pebbles'); * // => 'fred, barney, & pebbles' */ function escape(string) { string = toString(string); return (string && reHasUnescapedHtml.test(string)) ? string.replace(reUnescapedHtml, escapeHtmlChar) : string; } /** * Escapes the `RegExp` special characters "^", "$", "\", ".", "*", "+", * "?", "(", ")", "[", "]", "{", "}", and "|" in `string`. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to escape. * @returns {string} Returns the escaped string. * @example * * _.escapeRegExp('[lodash](https://lodash.com/)'); * // => '\[lodash\]\(https://lodash\.com/\)' */ function escapeRegExp(string) { string = toString(string); return (string && reHasRegExpChar.test(string)) ? string.replace(reRegExpChar, '\\$&') : string; } /** * Converts `string` to * [kebab case](https://en.wikipedia.org/wiki/Letter_case#Special_case_styles). * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the kebab cased string. * @example * * _.kebabCase('Foo Bar'); * // => 'foo-bar' * * _.kebabCase('fooBar'); * // => 'foo-bar' * * _.kebabCase('__FOO_BAR__'); * // => 'foo-bar' */ var kebabCase = createCompounder(function(result, word, index) { return result + (index ? '-' : '') + word.toLowerCase(); }); /** * Converts `string`, as space separated words, to lower case. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the lower cased string. * @example * * _.lowerCase('--Foo-Bar--'); * // => 'foo bar' * * _.lowerCase('fooBar'); * // => 'foo bar' * * _.lowerCase('__FOO_BAR__'); * // => 'foo bar' */ var lowerCase = createCompounder(function(result, word, index) { return result + (index ? ' ' : '') + word.toLowerCase(); }); /** * Converts the first character of `string` to lower case. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the converted string. * @example * * _.lowerFirst('Fred'); * // => 'fred' * * _.lowerFirst('FRED'); * // => 'fRED' */ var lowerFirst = createCaseFirst('toLowerCase'); /** * Pads `string` on the left and right sides if it's shorter than `length`. * Padding characters are truncated if they can't be evenly divided by `length`. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to pad. * @param {number} [length=0] The padding length. * @param {string} [chars=' '] The string used as padding. * @returns {string} Returns the padded string. * @example * * _.pad('abc', 8); * // => ' abc ' * * _.pad('abc', 8, '_-'); * // => '_-abc_-_' * * _.pad('abc', 3); * // => 'abc' */ function pad(string, length, chars) { string = toString(string); length = toInteger(length); var strLength = length ? stringSize(string) : 0; if (!length || strLength >= length) { return string; } var mid = (length - strLength) / 2; return ( createPadding(nativeFloor(mid), chars) + string + createPadding(nativeCeil(mid), chars) ); } /** * Pads `string` on the right side if it's shorter than `length`. Padding * characters are truncated if they exceed `length`. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to pad. * @param {number} [length=0] The padding length. * @param {string} [chars=' '] The string used as padding. * @returns {string} Returns the padded string. * @example * * _.padEnd('abc', 6); * // => 'abc ' * * _.padEnd('abc', 6, '_-'); * // => 'abc_-_' * * _.padEnd('abc', 3); * // => 'abc' */ function padEnd(string, length, chars) { string = toString(string); length = toInteger(length); var strLength = length ? stringSize(string) : 0; return (length && strLength < length) ? (string + createPadding(length - strLength, chars)) : string; } /** * Pads `string` on the left side if it's shorter than `length`. Padding * characters are truncated if they exceed `length`. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to pad. * @param {number} [length=0] The padding length. * @param {string} [chars=' '] The string used as padding. * @returns {string} Returns the padded string. * @example * * _.padStart('abc', 6); * // => ' abc' * * _.padStart('abc', 6, '_-'); * // => '_-_abc' * * _.padStart('abc', 3); * // => 'abc' */ function padStart(string, length, chars) { string = toString(string); length = toInteger(length); var strLength = length ? stringSize(string) : 0; return (length && strLength < length) ? (createPadding(length - strLength, chars) + string) : string; } /** * Converts `string` to an integer of the specified radix. If `radix` is * `undefined` or `0`, a `radix` of `10` is used unless `value` is a * hexadecimal, in which case a `radix` of `16` is used. * * **Note:** This method aligns with the * [ES5 implementation](https://es5.github.io/#x15.1.2.2) of `parseInt`. * * @static * @memberOf _ * @since 1.1.0 * @category String * @param {string} string The string to convert. * @param {number} [radix=10] The radix to interpret `value` by. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {number} Returns the converted integer. * @example * * _.parseInt('08'); * // => 8 * * _.map(['6', '08', '10'], _.parseInt); * // => [6, 8, 10] */ function parseInt(string, radix, guard) { if (guard || radix == null) { radix = 0; } else if (radix) { radix = +radix; } return nativeParseInt(toString(string).replace(reTrimStart, ''), radix || 0); } /** * Repeats the given string `n` times. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to repeat. * @param {number} [n=1] The number of times to repeat the string. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {string} Returns the repeated string. * @example * * _.repeat('*', 3); * // => '***' * * _.repeat('abc', 2); * // => 'abcabc' * * _.repeat('abc', 0); * // => '' */ function repeat(string, n, guard) { if ((guard ? isIterateeCall(string, n, guard) : n === undefined)) { n = 1; } else { n = toInteger(n); } return baseRepeat(toString(string), n); } /** * Replaces matches for `pattern` in `string` with `replacement`. * * **Note:** This method is based on * [`String#replace`](https://mdn.io/String/replace). * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to modify. * @param {RegExp|string} pattern The pattern to replace. * @param {Function|string} replacement The match replacement. * @returns {string} Returns the modified string. * @example * * _.replace('Hi Fred', 'Fred', 'Barney'); * // => 'Hi Barney' */ function replace() { var args = arguments, string = toString(args[0]); return args.length < 3 ? string : string.replace(args[1], args[2]); } /** * Converts `string` to * [snake case](https://en.wikipedia.org/wiki/Snake_case). * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the snake cased string. * @example * * _.snakeCase('Foo Bar'); * // => 'foo_bar' * * _.snakeCase('fooBar'); * // => 'foo_bar' * * _.snakeCase('--FOO-BAR--'); * // => 'foo_bar' */ var snakeCase = createCompounder(function(result, word, index) { return result + (index ? '_' : '') + word.toLowerCase(); }); /** * Splits `string` by `separator`. * * **Note:** This method is based on * [`String#split`](https://mdn.io/String/split). * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to split. * @param {RegExp|string} separator The separator pattern to split by. * @param {number} [limit] The length to truncate results to. * @returns {Array} Returns the string segments. * @example * * _.split('a-b-c', '-', 2); * // => ['a', 'b'] */ function split(string, separator, limit) { if (limit && typeof limit != 'number' && isIterateeCall(string, separator, limit)) { separator = limit = undefined; } limit = limit === undefined ? MAX_ARRAY_LENGTH : limit >>> 0; if (!limit) { return []; } string = toString(string); if (string && ( typeof separator == 'string' || (separator != null && !isRegExp(separator)) )) { separator = baseToString(separator); if (!separator && hasUnicode(string)) { return castSlice(stringToArray(string), 0, limit); } } return string.split(separator, limit); } /** * Converts `string` to * [start case](https://en.wikipedia.org/wiki/Letter_case#Stylistic_or_specialised_usage). * * @static * @memberOf _ * @since 3.1.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the start cased string. * @example * * _.startCase('--foo-bar--'); * // => 'Foo Bar' * * _.startCase('fooBar'); * // => 'Foo Bar' * * _.startCase('__FOO_BAR__'); * // => 'FOO BAR' */ var startCase = createCompounder(function(result, word, index) { return result + (index ? ' ' : '') + upperFirst(word); }); /** * Checks if `string` starts with the given target string. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to inspect. * @param {string} [target] The string to search for. * @param {number} [position=0] The position to search from. * @returns {boolean} Returns `true` if `string` starts with `target`, * else `false`. * @example * * _.startsWith('abc', 'a'); * // => true * * _.startsWith('abc', 'b'); * // => false * * _.startsWith('abc', 'b', 1); * // => true */ function startsWith(string, target, position) { string = toString(string); position = position == null ? 0 : baseClamp(toInteger(position), 0, string.length); target = baseToString(target); return string.slice(position, position + target.length) == target; } /** * Creates a compiled template function that can interpolate data properties * in "interpolate" delimiters, HTML-escape interpolated data properties in * "escape" delimiters, and execute JavaScript in "evaluate" delimiters. Data * properties may be accessed as free variables in the template. If a setting * object is given, it takes precedence over `_.templateSettings` values. * * **Note:** In the development build `_.template` utilizes * [sourceURLs](http://www.html5rocks.com/en/tutorials/developertools/sourcemaps/#toc-sourceurl) * for easier debugging. * * For more information on precompiling templates see * [lodash's custom builds documentation](https://lodash.com/custom-builds). * * For more information on Chrome extension sandboxes see * [Chrome's extensions documentation](https://developer.chrome.com/extensions/sandboxingEval). * * @static * @since 0.1.0 * @memberOf _ * @category String * @param {string} [string=''] The template string. * @param {Object} [options={}] The options object. * @param {RegExp} [options.escape=_.templateSettings.escape] * The HTML "escape" delimiter. * @param {RegExp} [options.evaluate=_.templateSettings.evaluate] * The "evaluate" delimiter. * @param {Object} [options.imports=_.templateSettings.imports] * An object to import into the template as free variables. * @param {RegExp} [options.interpolate=_.templateSettings.interpolate] * The "interpolate" delimiter. * @param {string} [options.sourceURL='lodash.templateSources[n]'] * The sourceURL of the compiled template. * @param {string} [options.variable='obj'] * The data object variable name. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Function} Returns the compiled template function. * @example * * // Use the "interpolate" delimiter to create a compiled template. * var compiled = _.template('hello <%= user %>!'); * compiled({ 'user': 'fred' }); * // => 'hello fred!' * * // Use the HTML "escape" delimiter to escape data property values. * var compiled = _.template('<b><%- value %></b>'); * compiled({ 'value': '<script>' }); * // => '<b><script></b>' * * // Use the "evaluate" delimiter to execute JavaScript and generate HTML. * var compiled = _.template('<% _.forEach(users, function(user) { %><li><%- user %></li><% }); %>'); * compiled({ 'users': ['fred', 'barney'] }); * // => '<li>fred</li><li>barney</li>' * * // Use the internal `print` function in "evaluate" delimiters. * var compiled = _.template('<% print("hello " + user); %>!'); * compiled({ 'user': 'barney' }); * // => 'hello barney!' * * // Use the ES template literal delimiter as an "interpolate" delimiter. * // Disable support by replacing the "interpolate" delimiter. * var compiled = _.template('hello ${ user }!'); * compiled({ 'user': 'pebbles' }); * // => 'hello pebbles!' * * // Use backslashes to treat delimiters as plain text. * var compiled = _.template('<%= "\\<%- value %\\>" %>'); * compiled({ 'value': 'ignored' }); * // => '<%- value %>' * * // Use the `imports` option to import `jQuery` as `jq`. * var text = '<% jq.each(users, function(user) { %><li><%- user %></li><% }); %>'; * var compiled = _.template(text, { 'imports': { 'jq': jQuery } }); * compiled({ 'users': ['fred', 'barney'] }); * // => '<li>fred</li><li>barney</li>' * * // Use the `sourceURL` option to specify a custom sourceURL for the template. * var compiled = _.template('hello <%= user %>!', { 'sourceURL': '/basic/greeting.jst' }); * compiled(data); * // => Find the source of "greeting.jst" under the Sources tab or Resources panel of the web inspector. * * // Use the `variable` option to ensure a with-statement isn't used in the compiled template. * var compiled = _.template('hi <%= data.user %>!', { 'variable': 'data' }); * compiled.source; * // => function(data) { * // var __t, __p = ''; * // __p += 'hi ' + ((__t = ( data.user )) == null ? '' : __t) + '!'; * // return __p; * // } * * // Use custom template delimiters. * _.templateSettings.interpolate = /{{([\s\S]+?)}}/g; * var compiled = _.template('hello {{ user }}!'); * compiled({ 'user': 'mustache' }); * // => 'hello mustache!' * * // Use the `source` property to inline compiled templates for meaningful * // line numbers in error messages and stack traces. * fs.writeFileSync(path.join(process.cwd(), 'jst.js'), '\ * var JST = {\ * "main": ' + _.template(mainText).source + '\ * };\ * '); */ function template(string, options, guard) { // Based on John Resig's `tmpl` implementation // (http://ejohn.org/blog/javascript-micro-templating/) // and Laura Doktorova's doT.js (https://github.com/olado/doT). var settings = lodash.templateSettings; if (guard && isIterateeCall(string, options, guard)) { options = undefined; } string = toString(string); options = assignInWith({}, options, settings, customDefaultsAssignIn); var imports = assignInWith({}, options.imports, settings.imports, customDefaultsAssignIn), importsKeys = keys(imports), importsValues = baseValues(imports, importsKeys); var isEscaping, isEvaluating, index = 0, interpolate = options.interpolate || reNoMatch, source = "__p += '"; // Compile the regexp to match each delimiter. var reDelimiters = RegExp( (options.escape || reNoMatch).source + '|' + interpolate.source + '|' + (interpolate === reInterpolate ? reEsTemplate : reNoMatch).source + '|' + (options.evaluate || reNoMatch).source + '|$' , 'g'); // Use a sourceURL for easier debugging. var sourceURL = '//# sourceURL=' + ('sourceURL' in options ? options.sourceURL : ('lodash.templateSources[' + (++templateCounter) + ']') ) + '\n'; string.replace(reDelimiters, function(match, escapeValue, interpolateValue, esTemplateValue, evaluateValue, offset) { interpolateValue || (interpolateValue = esTemplateValue); // Escape characters that can't be included in string literals. source += string.slice(index, offset).replace(reUnescapedString, escapeStringChar); // Replace delimiters with snippets. if (escapeValue) { isEscaping = true; source += "' +\n__e(" + escapeValue + ") +\n'"; } if (evaluateValue) { isEvaluating = true; source += "';\n" + evaluateValue + ";\n__p += '"; } if (interpolateValue) { source += "' +\n((__t = (" + interpolateValue + ")) == null ? '' : __t) +\n'"; } index = offset + match.length; // The JS engine embedded in Adobe products needs `match` returned in // order to produce the correct `offset` value. return match; }); source += "';\n"; // If `variable` is not specified wrap a with-statement around the generated // code to add the data object to the top of the scope chain. var variable = options.variable; if (!variable) { source = 'with (obj) {\n' + source + '\n}\n'; } // Cleanup code by stripping empty strings. source = (isEvaluating ? source.replace(reEmptyStringLeading, '') : source) .replace(reEmptyStringMiddle, '$1') .replace(reEmptyStringTrailing, '$1;'); // Frame code as the function body. source = 'function(' + (variable || 'obj') + ') {\n' + (variable ? '' : 'obj || (obj = {});\n' ) + "var __t, __p = ''" + (isEscaping ? ', __e = _.escape' : '' ) + (isEvaluating ? ', __j = Array.prototype.join;\n' + "function print() { __p += __j.call(arguments, '') }\n" : ';\n' ) + source + 'return __p\n}'; var result = attempt(function() { return Function(importsKeys, sourceURL + 'return ' + source) .apply(undefined, importsValues); }); // Provide the compiled function's source by its `toString` method or // the `source` property as a convenience for inlining compiled templates. result.source = source; if (isError(result)) { throw result; } return result; } /** * Converts `string`, as a whole, to lower case just like * [String#toLowerCase](https://mdn.io/toLowerCase). * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the lower cased string. * @example * * _.toLower('--Foo-Bar--'); * // => '--foo-bar--' * * _.toLower('fooBar'); * // => 'foobar' * * _.toLower('__FOO_BAR__'); * // => '__foo_bar__' */ function toLower(value) { return toString(value).toLowerCase(); } /** * Converts `string`, as a whole, to upper case just like * [String#toUpperCase](https://mdn.io/toUpperCase). * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the upper cased string. * @example * * _.toUpper('--foo-bar--'); * // => '--FOO-BAR--' * * _.toUpper('fooBar'); * // => 'FOOBAR' * * _.toUpper('__foo_bar__'); * // => '__FOO_BAR__' */ function toUpper(value) { return toString(value).toUpperCase(); } /** * Removes leading and trailing whitespace or specified characters from `string`. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to trim. * @param {string} [chars=whitespace] The characters to trim. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {string} Returns the trimmed string. * @example * * _.trim(' abc '); * // => 'abc' * * _.trim('-_-abc-_-', '_-'); * // => 'abc' * * _.map([' foo ', ' bar '], _.trim); * // => ['foo', 'bar'] */ function trim(string, chars, guard) { string = toString(string); if (string && (guard || chars === undefined)) { return string.replace(reTrim, ''); } if (!string || !(chars = baseToString(chars))) { return string; } var strSymbols = stringToArray(string), chrSymbols = stringToArray(chars), start = charsStartIndex(strSymbols, chrSymbols), end = charsEndIndex(strSymbols, chrSymbols) + 1; return castSlice(strSymbols, start, end).join(''); } /** * Removes trailing whitespace or specified characters from `string`. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to trim. * @param {string} [chars=whitespace] The characters to trim. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {string} Returns the trimmed string. * @example * * _.trimEnd(' abc '); * // => ' abc' * * _.trimEnd('-_-abc-_-', '_-'); * // => '-_-abc' */ function trimEnd(string, chars, guard) { string = toString(string); if (string && (guard || chars === undefined)) { return string.replace(reTrimEnd, ''); } if (!string || !(chars = baseToString(chars))) { return string; } var strSymbols = stringToArray(string), end = charsEndIndex(strSymbols, stringToArray(chars)) + 1; return castSlice(strSymbols, 0, end).join(''); } /** * Removes leading whitespace or specified characters from `string`. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to trim. * @param {string} [chars=whitespace] The characters to trim. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {string} Returns the trimmed string. * @example * * _.trimStart(' abc '); * // => 'abc ' * * _.trimStart('-_-abc-_-', '_-'); * // => 'abc-_-' */ function trimStart(string, chars, guard) { string = toString(string); if (string && (guard || chars === undefined)) { return string.replace(reTrimStart, ''); } if (!string || !(chars = baseToString(chars))) { return string; } var strSymbols = stringToArray(string), start = charsStartIndex(strSymbols, stringToArray(chars)); return castSlice(strSymbols, start).join(''); } /** * Truncates `string` if it's longer than the given maximum string length. * The last characters of the truncated string are replaced with the omission * string which defaults to "...". * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to truncate. * @param {Object} [options={}] The options object. * @param {number} [options.length=30] The maximum string length. * @param {string} [options.omission='...'] The string to indicate text is omitted. * @param {RegExp|string} [options.separator] The separator pattern to truncate to. * @returns {string} Returns the truncated string. * @example * * _.truncate('hi-diddly-ho there, neighborino'); * // => 'hi-diddly-ho there, neighbo...' * * _.truncate('hi-diddly-ho there, neighborino', { * 'length': 24, * 'separator': ' ' * }); * // => 'hi-diddly-ho there,...' * * _.truncate('hi-diddly-ho there, neighborino', { * 'length': 24, * 'separator': /,? +/ * }); * // => 'hi-diddly-ho there...' * * _.truncate('hi-diddly-ho there, neighborino', { * 'omission': ' [...]' * }); * // => 'hi-diddly-ho there, neig [...]' */ function truncate(string, options) { var length = DEFAULT_TRUNC_LENGTH, omission = DEFAULT_TRUNC_OMISSION; if (isObject(options)) { var separator = 'separator' in options ? options.separator : separator; length = 'length' in options ? toInteger(options.length) : length; omission = 'omission' in options ? baseToString(options.omission) : omission; } string = toString(string); var strLength = string.length; if (hasUnicode(string)) { var strSymbols = stringToArray(string); strLength = strSymbols.length; } if (length >= strLength) { return string; } var end = length - stringSize(omission); if (end < 1) { return omission; } var result = strSymbols ? castSlice(strSymbols, 0, end).join('') : string.slice(0, end); if (separator === undefined) { return result + omission; } if (strSymbols) { end += (result.length - end); } if (isRegExp(separator)) { if (string.slice(end).search(separator)) { var match, substring = result; if (!separator.global) { separator = RegExp(separator.source, toString(reFlags.exec(separator)) + 'g'); } separator.lastIndex = 0; while ((match = separator.exec(substring))) { var newEnd = match.index; } result = result.slice(0, newEnd === undefined ? end : newEnd); } } else if (string.indexOf(baseToString(separator), end) != end) { var index = result.lastIndexOf(separator); if (index > -1) { result = result.slice(0, index); } } return result + omission; } /** * The inverse of `_.escape`; this method converts the HTML entities * `&`, `<`, `>`, `"`, and `'` in `string` to * their corresponding characters. * * **Note:** No other HTML entities are unescaped. To unescape additional * HTML entities use a third-party library like [_he_](https://mths.be/he). * * @static * @memberOf _ * @since 0.6.0 * @category String * @param {string} [string=''] The string to unescape. * @returns {string} Returns the unescaped string. * @example * * _.unescape('fred, barney, & pebbles'); * // => 'fred, barney, & pebbles' */ function unescape(string) { string = toString(string); return (string && reHasEscapedHtml.test(string)) ? string.replace(reEscapedHtml, unescapeHtmlChar) : string; } /** * Converts `string`, as space separated words, to upper case. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the upper cased string. * @example * * _.upperCase('--foo-bar'); * // => 'FOO BAR' * * _.upperCase('fooBar'); * // => 'FOO BAR' * * _.upperCase('__foo_bar__'); * // => 'FOO BAR' */ var upperCase = createCompounder(function(result, word, index) { return result + (index ? ' ' : '') + word.toUpperCase(); }); /** * Converts the first character of `string` to upper case. * * @static * @memberOf _ * @since 4.0.0 * @category String * @param {string} [string=''] The string to convert. * @returns {string} Returns the converted string. * @example * * _.upperFirst('fred'); * // => 'Fred' * * _.upperFirst('FRED'); * // => 'FRED' */ var upperFirst = createCaseFirst('toUpperCase'); /** * Splits `string` into an array of its words. * * @static * @memberOf _ * @since 3.0.0 * @category String * @param {string} [string=''] The string to inspect. * @param {RegExp|string} [pattern] The pattern to match words. * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. * @returns {Array} Returns the words of `string`. * @example * * _.words('fred, barney, & pebbles'); * // => ['fred', 'barney', 'pebbles'] * * _.words('fred, barney, & pebbles', /[^, ]+/g); * // => ['fred', 'barney', '&', 'pebbles'] */ function words(string, pattern, guard) { string = toString(string); pattern = guard ? undefined : pattern; if (pattern === undefined) { return hasUnicodeWord(string) ? unicodeWords(string) : asciiWords(string); } return string.match(pattern) || []; } /*------------------------------------------------------------------------*/ /** * Attempts to invoke `func`, returning either the result or the caught error * object. Any additional arguments are provided to `func` when it's invoked. * * @static * @memberOf _ * @since 3.0.0 * @category Util * @param {Function} func The function to attempt. * @param {...*} [args] The arguments to invoke `func` with. * @returns {*} Returns the `func` result or error object. * @example * * // Avoid throwing errors for invalid selectors. * var elements = _.attempt(function(selector) { * return document.querySelectorAll(selector); * }, '>_>'); * * if (_.isError(elements)) { * elements = []; * } */ var attempt = baseRest(function(func, args) { try { return apply(func, undefined, args); } catch (e) { return isError(e) ? e : new Error(e); } }); /** * Binds methods of an object to the object itself, overwriting the existing * method. * * **Note:** This method doesn't set the "length" property of bound functions. * * @static * @since 0.1.0 * @memberOf _ * @category Util * @param {Object} object The object to bind and assign the bound methods to. * @param {...(string|string[])} methodNames The object method names to bind. * @returns {Object} Returns `object`. * @example * * var view = { * 'label': 'docs', * 'click': function() { * console.log('clicked ' + this.label); * } * }; * * _.bindAll(view, ['click']); * jQuery(element).on('click', view.click); * // => Logs 'clicked docs' when clicked. */ var bindAll = flatRest(function(object, methodNames) { arrayEach(methodNames, function(key) { key = toKey(key); baseAssignValue(object, key, bind(object[key], object)); }); return object; }); /** * Creates a function that iterates over `pairs` and invokes the corresponding * function of the first predicate to return truthy. The predicate-function * pairs are invoked with the `this` binding and arguments of the created * function. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {Array} pairs The predicate-function pairs. * @returns {Function} Returns the new composite function. * @example * * var func = _.cond([ * [_.matches({ 'a': 1 }), _.constant('matches A')], * [_.conforms({ 'b': _.isNumber }), _.constant('matches B')], * [_.stubTrue, _.constant('no match')] * ]); * * func({ 'a': 1, 'b': 2 }); * // => 'matches A' * * func({ 'a': 0, 'b': 1 }); * // => 'matches B' * * func({ 'a': '1', 'b': '2' }); * // => 'no match' */ function cond(pairs) { var length = pairs == null ? 0 : pairs.length, toIteratee = getIteratee(); pairs = !length ? [] : arrayMap(pairs, function(pair) { if (typeof pair[1] != 'function') { throw new TypeError(FUNC_ERROR_TEXT); } return [toIteratee(pair[0]), pair[1]]; }); return baseRest(function(args) { var index = -1; while (++index < length) { var pair = pairs[index]; if (apply(pair[0], this, args)) { return apply(pair[1], this, args); } } }); } /** * Creates a function that invokes the predicate properties of `source` with * the corresponding property values of a given object, returning `true` if * all predicates return truthy, else `false`. * * **Note:** The created function is equivalent to `_.conformsTo` with * `source` partially applied. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {Object} source The object of property predicates to conform to. * @returns {Function} Returns the new spec function. * @example * * var objects = [ * { 'a': 2, 'b': 1 }, * { 'a': 1, 'b': 2 } * ]; * * _.filter(objects, _.conforms({ 'b': function(n) { return n > 1; } })); * // => [{ 'a': 1, 'b': 2 }] */ function conforms(source) { return baseConforms(baseClone(source, CLONE_DEEP_FLAG)); } /** * Creates a function that returns `value`. * * @static * @memberOf _ * @since 2.4.0 * @category Util * @param {*} value The value to return from the new function. * @returns {Function} Returns the new constant function. * @example * * var objects = _.times(2, _.constant({ 'a': 1 })); * * console.log(objects); * // => [{ 'a': 1 }, { 'a': 1 }] * * console.log(objects[0] === objects[1]); * // => true */ function constant(value) { return function() { return value; }; } /** * Checks `value` to determine whether a default value should be returned in * its place. The `defaultValue` is returned if `value` is `NaN`, `null`, * or `undefined`. * * @static * @memberOf _ * @since 4.14.0 * @category Util * @param {*} value The value to check. * @param {*} defaultValue The default value. * @returns {*} Returns the resolved value. * @example * * _.defaultTo(1, 10); * // => 1 * * _.defaultTo(undefined, 10); * // => 10 */ function defaultTo(value, defaultValue) { return (value == null || value !== value) ? defaultValue : value; } /** * Creates a function that returns the result of invoking the given functions * with the `this` binding of the created function, where each successive * invocation is supplied the return value of the previous. * * @static * @memberOf _ * @since 3.0.0 * @category Util * @param {...(Function|Function[])} [funcs] The functions to invoke. * @returns {Function} Returns the new composite function. * @see _.flowRight * @example * * function square(n) { * return n * n; * } * * var addSquare = _.flow([_.add, square]); * addSquare(1, 2); * // => 9 */ var flow = createFlow(); /** * This method is like `_.flow` except that it creates a function that * invokes the given functions from right to left. * * @static * @since 3.0.0 * @memberOf _ * @category Util * @param {...(Function|Function[])} [funcs] The functions to invoke. * @returns {Function} Returns the new composite function. * @see _.flow * @example * * function square(n) { * return n * n; * } * * var addSquare = _.flowRight([square, _.add]); * addSquare(1, 2); * // => 9 */ var flowRight = createFlow(true); /** * This method returns the first argument it receives. * * @static * @since 0.1.0 * @memberOf _ * @category Util * @param {*} value Any value. * @returns {*} Returns `value`. * @example * * var object = { 'a': 1 }; * * console.log(_.identity(object) === object); * // => true */ function identity(value) { return value; } /** * Creates a function that invokes `func` with the arguments of the created * function. If `func` is a property name, the created function returns the * property value for a given element. If `func` is an array or object, the * created function returns `true` for elements that contain the equivalent * source properties, otherwise it returns `false`. * * @static * @since 4.0.0 * @memberOf _ * @category Util * @param {*} [func=_.identity] The value to convert to a callback. * @returns {Function} Returns the callback. * @example * * var users = [ * { 'user': 'barney', 'age': 36, 'active': true }, * { 'user': 'fred', 'age': 40, 'active': false } * ]; * * // The `_.matches` iteratee shorthand. * _.filter(users, _.iteratee({ 'user': 'barney', 'active': true })); * // => [{ 'user': 'barney', 'age': 36, 'active': true }] * * // The `_.matchesProperty` iteratee shorthand. * _.filter(users, _.iteratee(['user', 'fred'])); * // => [{ 'user': 'fred', 'age': 40 }] * * // The `_.property` iteratee shorthand. * _.map(users, _.iteratee('user')); * // => ['barney', 'fred'] * * // Create custom iteratee shorthands. * _.iteratee = _.wrap(_.iteratee, function(iteratee, func) { * return !_.isRegExp(func) ? iteratee(func) : function(string) { * return func.test(string); * }; * }); * * _.filter(['abc', 'def'], /ef/); * // => ['def'] */ function iteratee(func) { return baseIteratee(typeof func == 'function' ? func : baseClone(func, CLONE_DEEP_FLAG)); } /** * Creates a function that performs a partial deep comparison between a given * object and `source`, returning `true` if the given object has equivalent * property values, else `false`. * * **Note:** The created function is equivalent to `_.isMatch` with `source` * partially applied. * * Partial comparisons will match empty array and empty object `source` * values against any array or object value, respectively. See `_.isEqual` * for a list of supported value comparisons. * * @static * @memberOf _ * @since 3.0.0 * @category Util * @param {Object} source The object of property values to match. * @returns {Function} Returns the new spec function. * @example * * var objects = [ * { 'a': 1, 'b': 2, 'c': 3 }, * { 'a': 4, 'b': 5, 'c': 6 } * ]; * * _.filter(objects, _.matches({ 'a': 4, 'c': 6 })); * // => [{ 'a': 4, 'b': 5, 'c': 6 }] */ function matches(source) { return baseMatches(baseClone(source, CLONE_DEEP_FLAG)); } /** * Creates a function that performs a partial deep comparison between the * value at `path` of a given object to `srcValue`, returning `true` if the * object value is equivalent, else `false`. * * **Note:** Partial comparisons will match empty array and empty object * `srcValue` values against any array or object value, respectively. See * `_.isEqual` for a list of supported value comparisons. * * @static * @memberOf _ * @since 3.2.0 * @category Util * @param {Array|string} path The path of the property to get. * @param {*} srcValue The value to match. * @returns {Function} Returns the new spec function. * @example * * var objects = [ * { 'a': 1, 'b': 2, 'c': 3 }, * { 'a': 4, 'b': 5, 'c': 6 } * ]; * * _.find(objects, _.matchesProperty('a', 4)); * // => { 'a': 4, 'b': 5, 'c': 6 } */ function matchesProperty(path, srcValue) { return baseMatchesProperty(path, baseClone(srcValue, CLONE_DEEP_FLAG)); } /** * Creates a function that invokes the method at `path` of a given object. * Any additional arguments are provided to the invoked method. * * @static * @memberOf _ * @since 3.7.0 * @category Util * @param {Array|string} path The path of the method to invoke. * @param {...*} [args] The arguments to invoke the method with. * @returns {Function} Returns the new invoker function. * @example * * var objects = [ * { 'a': { 'b': _.constant(2) } }, * { 'a': { 'b': _.constant(1) } } * ]; * * _.map(objects, _.method('a.b')); * // => [2, 1] * * _.map(objects, _.method(['a', 'b'])); * // => [2, 1] */ var method = baseRest(function(path, args) { return function(object) { return baseInvoke(object, path, args); }; }); /** * The opposite of `_.method`; this method creates a function that invokes * the method at a given path of `object`. Any additional arguments are * provided to the invoked method. * * @static * @memberOf _ * @since 3.7.0 * @category Util * @param {Object} object The object to query. * @param {...*} [args] The arguments to invoke the method with. * @returns {Function} Returns the new invoker function. * @example * * var array = _.times(3, _.constant), * object = { 'a': array, 'b': array, 'c': array }; * * _.map(['a[2]', 'c[0]'], _.methodOf(object)); * // => [2, 0] * * _.map([['a', '2'], ['c', '0']], _.methodOf(object)); * // => [2, 0] */ var methodOf = baseRest(function(object, args) { return function(path) { return baseInvoke(object, path, args); }; }); /** * Adds all own enumerable string keyed function properties of a source * object to the destination object. If `object` is a function, then methods * are added to its prototype as well. * * **Note:** Use `_.runInContext` to create a pristine `lodash` function to * avoid conflicts caused by modifying the original. * * @static * @since 0.1.0 * @memberOf _ * @category Util * @param {Function|Object} [object=lodash] The destination object. * @param {Object} source The object of functions to add. * @param {Object} [options={}] The options object. * @param {boolean} [options.chain=true] Specify whether mixins are chainable. * @returns {Function|Object} Returns `object`. * @example * * function vowels(string) { * return _.filter(string, function(v) { * return /[aeiou]/i.test(v); * }); * } * * _.mixin({ 'vowels': vowels }); * _.vowels('fred'); * // => ['e'] * * _('fred').vowels().value(); * // => ['e'] * * _.mixin({ 'vowels': vowels }, { 'chain': false }); * _('fred').vowels(); * // => ['e'] */ function mixin(object, source, options) { var props = keys(source), methodNames = baseFunctions(source, props); if (options == null && !(isObject(source) && (methodNames.length || !props.length))) { options = source; source = object; object = this; methodNames = baseFunctions(source, keys(source)); } var chain = !(isObject(options) && 'chain' in options) || !!options.chain, isFunc = isFunction(object); arrayEach(methodNames, function(methodName) { var func = source[methodName]; object[methodName] = func; if (isFunc) { object.prototype[methodName] = function() { var chainAll = this.__chain__; if (chain || chainAll) { var result = object(this.__wrapped__), actions = result.__actions__ = copyArray(this.__actions__); actions.push({ 'func': func, 'args': arguments, 'thisArg': object }); result.__chain__ = chainAll; return result; } return func.apply(object, arrayPush([this.value()], arguments)); }; } }); return object; } /** * Reverts the `_` variable to its previous value and returns a reference to * the `lodash` function. * * @static * @since 0.1.0 * @memberOf _ * @category Util * @returns {Function} Returns the `lodash` function. * @example * * var lodash = _.noConflict(); */ function noConflict() { if (root._ === this) { root._ = oldDash; } return this; } /** * This method returns `undefined`. * * @static * @memberOf _ * @since 2.3.0 * @category Util * @example * * _.times(2, _.noop); * // => [undefined, undefined] */ function noop() { // No operation performed. } /** * Creates a function that gets the argument at index `n`. If `n` is negative, * the nth argument from the end is returned. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {number} [n=0] The index of the argument to return. * @returns {Function} Returns the new pass-thru function. * @example * * var func = _.nthArg(1); * func('a', 'b', 'c', 'd'); * // => 'b' * * var func = _.nthArg(-2); * func('a', 'b', 'c', 'd'); * // => 'c' */ function nthArg(n) { n = toInteger(n); return baseRest(function(args) { return baseNth(args, n); }); } /** * Creates a function that invokes `iteratees` with the arguments it receives * and returns their results. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {...(Function|Function[])} [iteratees=[_.identity]] * The iteratees to invoke. * @returns {Function} Returns the new function. * @example * * var func = _.over([Math.max, Math.min]); * * func(1, 2, 3, 4); * // => [4, 1] */ var over = createOver(arrayMap); /** * Creates a function that checks if **all** of the `predicates` return * truthy when invoked with the arguments it receives. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {...(Function|Function[])} [predicates=[_.identity]] * The predicates to check. * @returns {Function} Returns the new function. * @example * * var func = _.overEvery([Boolean, isFinite]); * * func('1'); * // => true * * func(null); * // => false * * func(NaN); * // => false */ var overEvery = createOver(arrayEvery); /** * Creates a function that checks if **any** of the `predicates` return * truthy when invoked with the arguments it receives. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {...(Function|Function[])} [predicates=[_.identity]] * The predicates to check. * @returns {Function} Returns the new function. * @example * * var func = _.overSome([Boolean, isFinite]); * * func('1'); * // => true * * func(null); * // => true * * func(NaN); * // => false */ var overSome = createOver(arraySome); /** * Creates a function that returns the value at `path` of a given object. * * @static * @memberOf _ * @since 2.4.0 * @category Util * @param {Array|string} path The path of the property to get. * @returns {Function} Returns the new accessor function. * @example * * var objects = [ * { 'a': { 'b': 2 } }, * { 'a': { 'b': 1 } } * ]; * * _.map(objects, _.property('a.b')); * // => [2, 1] * * _.map(_.sortBy(objects, _.property(['a', 'b'])), 'a.b'); * // => [1, 2] */ function property(path) { return isKey(path) ? baseProperty(toKey(path)) : basePropertyDeep(path); } /** * The opposite of `_.property`; this method creates a function that returns * the value at a given path of `object`. * * @static * @memberOf _ * @since 3.0.0 * @category Util * @param {Object} object The object to query. * @returns {Function} Returns the new accessor function. * @example * * var array = [0, 1, 2], * object = { 'a': array, 'b': array, 'c': array }; * * _.map(['a[2]', 'c[0]'], _.propertyOf(object)); * // => [2, 0] * * _.map([['a', '2'], ['c', '0']], _.propertyOf(object)); * // => [2, 0] */ function propertyOf(object) { return function(path) { return object == null ? undefined : baseGet(object, path); }; } /** * Creates an array of numbers (positive and/or negative) progressing from * `start` up to, but not including, `end`. A step of `-1` is used if a negative * `start` is specified without an `end` or `step`. If `end` is not specified, * it's set to `start` with `start` then set to `0`. * * **Note:** JavaScript follows the IEEE-754 standard for resolving * floating-point values which can produce unexpected results. * * @static * @since 0.1.0 * @memberOf _ * @category Util * @param {number} [start=0] The start of the range. * @param {number} end The end of the range. * @param {number} [step=1] The value to increment or decrement by. * @returns {Array} Returns the range of numbers. * @see _.inRange, _.rangeRight * @example * * _.range(4); * // => [0, 1, 2, 3] * * _.range(-4); * // => [0, -1, -2, -3] * * _.range(1, 5); * // => [1, 2, 3, 4] * * _.range(0, 20, 5); * // => [0, 5, 10, 15] * * _.range(0, -4, -1); * // => [0, -1, -2, -3] * * _.range(1, 4, 0); * // => [1, 1, 1] * * _.range(0); * // => [] */ var range = createRange(); /** * This method is like `_.range` except that it populates values in * descending order. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {number} [start=0] The start of the range. * @param {number} end The end of the range. * @param {number} [step=1] The value to increment or decrement by. * @returns {Array} Returns the range of numbers. * @see _.inRange, _.range * @example * * _.rangeRight(4); * // => [3, 2, 1, 0] * * _.rangeRight(-4); * // => [-3, -2, -1, 0] * * _.rangeRight(1, 5); * // => [4, 3, 2, 1] * * _.rangeRight(0, 20, 5); * // => [15, 10, 5, 0] * * _.rangeRight(0, -4, -1); * // => [-3, -2, -1, 0] * * _.rangeRight(1, 4, 0); * // => [1, 1, 1] * * _.rangeRight(0); * // => [] */ var rangeRight = createRange(true); /** * This method returns a new empty array. * * @static * @memberOf _ * @since 4.13.0 * @category Util * @returns {Array} Returns the new empty array. * @example * * var arrays = _.times(2, _.stubArray); * * console.log(arrays); * // => [[], []] * * console.log(arrays[0] === arrays[1]); * // => false */ function stubArray() { return []; } /** * This method returns `false`. * * @static * @memberOf _ * @since 4.13.0 * @category Util * @returns {boolean} Returns `false`. * @example * * _.times(2, _.stubFalse); * // => [false, false] */ function stubFalse() { return false; } /** * This method returns a new empty object. * * @static * @memberOf _ * @since 4.13.0 * @category Util * @returns {Object} Returns the new empty object. * @example * * var objects = _.times(2, _.stubObject); * * console.log(objects); * // => [{}, {}] * * console.log(objects[0] === objects[1]); * // => false */ function stubObject() { return {}; } /** * This method returns an empty string. * * @static * @memberOf _ * @since 4.13.0 * @category Util * @returns {string} Returns the empty string. * @example * * _.times(2, _.stubString); * // => ['', ''] */ function stubString() { return ''; } /** * This method returns `true`. * * @static * @memberOf _ * @since 4.13.0 * @category Util * @returns {boolean} Returns `true`. * @example * * _.times(2, _.stubTrue); * // => [true, true] */ function stubTrue() { return true; } /** * Invokes the iteratee `n` times, returning an array of the results of * each invocation. The iteratee is invoked with one argument; (index). * * @static * @since 0.1.0 * @memberOf _ * @category Util * @param {number} n The number of times to invoke `iteratee`. * @param {Function} [iteratee=_.identity] The function invoked per iteration. * @returns {Array} Returns the array of results. * @example * * _.times(3, String); * // => ['0', '1', '2'] * * _.times(4, _.constant(0)); * // => [0, 0, 0, 0] */ function times(n, iteratee) { n = toInteger(n); if (n < 1 || n > MAX_SAFE_INTEGER) { return []; } var index = MAX_ARRAY_LENGTH, length = nativeMin(n, MAX_ARRAY_LENGTH); iteratee = getIteratee(iteratee); n -= MAX_ARRAY_LENGTH; var result = baseTimes(length, iteratee); while (++index < n) { iteratee(index); } return result; } /** * Converts `value` to a property path array. * * @static * @memberOf _ * @since 4.0.0 * @category Util * @param {*} value The value to convert. * @returns {Array} Returns the new property path array. * @example * * _.toPath('a.b.c'); * // => ['a', 'b', 'c'] * * _.toPath('a[0].b.c'); * // => ['a', '0', 'b', 'c'] */ function toPath(value) { if (isArray(value)) { return arrayMap(value, toKey); } return isSymbol(value) ? [value] : copyArray(stringToPath(toString(value))); } /** * Generates a unique ID. If `prefix` is given, the ID is appended to it. * * @static * @since 0.1.0 * @memberOf _ * @category Util * @param {string} [prefix=''] The value to prefix the ID with. * @returns {string} Returns the unique ID. * @example * * _.uniqueId('contact_'); * // => 'contact_104' * * _.uniqueId(); * // => '105' */ function uniqueId(prefix) { var id = ++idCounter; return toString(prefix) + id; } /*------------------------------------------------------------------------*/ /** * Adds two numbers. * * @static * @memberOf _ * @since 3.4.0 * @category Math * @param {number} augend The first number in an addition. * @param {number} addend The second number in an addition. * @returns {number} Returns the total. * @example * * _.add(6, 4); * // => 10 */ var add = createMathOperation(function(augend, addend) { return augend + addend; }, 0); /** * Computes `number` rounded up to `precision`. * * @static * @memberOf _ * @since 3.10.0 * @category Math * @param {number} number The number to round up. * @param {number} [precision=0] The precision to round up to. * @returns {number} Returns the rounded up number. * @example * * _.ceil(4.006); * // => 5 * * _.ceil(6.004, 2); * // => 6.01 * * _.ceil(6040, -2); * // => 6100 */ var ceil = createRound('ceil'); /** * Divide two numbers. * * @static * @memberOf _ * @since 4.7.0 * @category Math * @param {number} dividend The first number in a division. * @param {number} divisor The second number in a division. * @returns {number} Returns the quotient. * @example * * _.divide(6, 4); * // => 1.5 */ var divide = createMathOperation(function(dividend, divisor) { return dividend / divisor; }, 1); /** * Computes `number` rounded down to `precision`. * * @static * @memberOf _ * @since 3.10.0 * @category Math * @param {number} number The number to round down. * @param {number} [precision=0] The precision to round down to. * @returns {number} Returns the rounded down number. * @example * * _.floor(4.006); * // => 4 * * _.floor(0.046, 2); * // => 0.04 * * _.floor(4060, -2); * // => 4000 */ var floor = createRound('floor'); /** * Computes the maximum value of `array`. If `array` is empty or falsey, * `undefined` is returned. * * @static * @since 0.1.0 * @memberOf _ * @category Math * @param {Array} array The array to iterate over. * @returns {*} Returns the maximum value. * @example * * _.max([4, 2, 8, 6]); * // => 8 * * _.max([]); * // => undefined */ function max(array) { return (array && array.length) ? baseExtremum(array, identity, baseGt) : undefined; } /** * This method is like `_.max` except that it accepts `iteratee` which is * invoked for each element in `array` to generate the criterion by which * the value is ranked. The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Math * @param {Array} array The array to iterate over. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {*} Returns the maximum value. * @example * * var objects = [{ 'n': 1 }, { 'n': 2 }]; * * _.maxBy(objects, function(o) { return o.n; }); * // => { 'n': 2 } * * // The `_.property` iteratee shorthand. * _.maxBy(objects, 'n'); * // => { 'n': 2 } */ function maxBy(array, iteratee) { return (array && array.length) ? baseExtremum(array, getIteratee(iteratee, 2), baseGt) : undefined; } /** * Computes the mean of the values in `array`. * * @static * @memberOf _ * @since 4.0.0 * @category Math * @param {Array} array The array to iterate over. * @returns {number} Returns the mean. * @example * * _.mean([4, 2, 8, 6]); * // => 5 */ function mean(array) { return baseMean(array, identity); } /** * This method is like `_.mean` except that it accepts `iteratee` which is * invoked for each element in `array` to generate the value to be averaged. * The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.7.0 * @category Math * @param {Array} array The array to iterate over. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {number} Returns the mean. * @example * * var objects = [{ 'n': 4 }, { 'n': 2 }, { 'n': 8 }, { 'n': 6 }]; * * _.meanBy(objects, function(o) { return o.n; }); * // => 5 * * // The `_.property` iteratee shorthand. * _.meanBy(objects, 'n'); * // => 5 */ function meanBy(array, iteratee) { return baseMean(array, getIteratee(iteratee, 2)); } /** * Computes the minimum value of `array`. If `array` is empty or falsey, * `undefined` is returned. * * @static * @since 0.1.0 * @memberOf _ * @category Math * @param {Array} array The array to iterate over. * @returns {*} Returns the minimum value. * @example * * _.min([4, 2, 8, 6]); * // => 2 * * _.min([]); * // => undefined */ function min(array) { return (array && array.length) ? baseExtremum(array, identity, baseLt) : undefined; } /** * This method is like `_.min` except that it accepts `iteratee` which is * invoked for each element in `array` to generate the criterion by which * the value is ranked. The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Math * @param {Array} array The array to iterate over. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {*} Returns the minimum value. * @example * * var objects = [{ 'n': 1 }, { 'n': 2 }]; * * _.minBy(objects, function(o) { return o.n; }); * // => { 'n': 1 } * * // The `_.property` iteratee shorthand. * _.minBy(objects, 'n'); * // => { 'n': 1 } */ function minBy(array, iteratee) { return (array && array.length) ? baseExtremum(array, getIteratee(iteratee, 2), baseLt) : undefined; } /** * Multiply two numbers. * * @static * @memberOf _ * @since 4.7.0 * @category Math * @param {number} multiplier The first number in a multiplication. * @param {number} multiplicand The second number in a multiplication. * @returns {number} Returns the product. * @example * * _.multiply(6, 4); * // => 24 */ var multiply = createMathOperation(function(multiplier, multiplicand) { return multiplier * multiplicand; }, 1); /** * Computes `number` rounded to `precision`. * * @static * @memberOf _ * @since 3.10.0 * @category Math * @param {number} number The number to round. * @param {number} [precision=0] The precision to round to. * @returns {number} Returns the rounded number. * @example * * _.round(4.006); * // => 4 * * _.round(4.006, 2); * // => 4.01 * * _.round(4060, -2); * // => 4100 */ var round = createRound('round'); /** * Subtract two numbers. * * @static * @memberOf _ * @since 4.0.0 * @category Math * @param {number} minuend The first number in a subtraction. * @param {number} subtrahend The second number in a subtraction. * @returns {number} Returns the difference. * @example * * _.subtract(6, 4); * // => 2 */ var subtract = createMathOperation(function(minuend, subtrahend) { return minuend - subtrahend; }, 0); /** * Computes the sum of the values in `array`. * * @static * @memberOf _ * @since 3.4.0 * @category Math * @param {Array} array The array to iterate over. * @returns {number} Returns the sum. * @example * * _.sum([4, 2, 8, 6]); * // => 20 */ function sum(array) { return (array && array.length) ? baseSum(array, identity) : 0; } /** * This method is like `_.sum` except that it accepts `iteratee` which is * invoked for each element in `array` to generate the value to be summed. * The iteratee is invoked with one argument: (value). * * @static * @memberOf _ * @since 4.0.0 * @category Math * @param {Array} array The array to iterate over. * @param {Function} [iteratee=_.identity] The iteratee invoked per element. * @returns {number} Returns the sum. * @example * * var objects = [{ 'n': 4 }, { 'n': 2 }, { 'n': 8 }, { 'n': 6 }]; * * _.sumBy(objects, function(o) { return o.n; }); * // => 20 * * // The `_.property` iteratee shorthand. * _.sumBy(objects, 'n'); * // => 20 */ function sumBy(array, iteratee) { return (array && array.length) ? baseSum(array, getIteratee(iteratee, 2)) : 0; } /*------------------------------------------------------------------------*/ // Add methods that return wrapped values in chain sequences. lodash.after = after; lodash.ary = ary; lodash.assign = assign; lodash.assignIn = assignIn; lodash.assignInWith = assignInWith; lodash.assignWith = assignWith; lodash.at = at; lodash.before = before; lodash.bind = bind; lodash.bindAll = bindAll; lodash.bindKey = bindKey; lodash.castArray = castArray; lodash.chain = chain; lodash.chunk = chunk; lodash.compact = compact; lodash.concat = concat; lodash.cond = cond; lodash.conforms = conforms; lodash.constant = constant; lodash.countBy = countBy; lodash.create = create; lodash.curry = curry; lodash.curryRight = curryRight; lodash.debounce = debounce; lodash.defaults = defaults; lodash.defaultsDeep = defaultsDeep; lodash.defer = defer; lodash.delay = delay; lodash.difference = difference; lodash.differenceBy = differenceBy; lodash.differenceWith = differenceWith; lodash.drop = drop; lodash.dropRight = dropRight; lodash.dropRightWhile = dropRightWhile; lodash.dropWhile = dropWhile; lodash.fill = fill; lodash.filter = filter; lodash.flatMap = flatMap; lodash.flatMapDeep = flatMapDeep; lodash.flatMapDepth = flatMapDepth; lodash.flatten = flatten; lodash.flattenDeep = flattenDeep; lodash.flattenDepth = flattenDepth; lodash.flip = flip; lodash.flow = flow; lodash.flowRight = flowRight; lodash.fromPairs = fromPairs; lodash.functions = functions; lodash.functionsIn = functionsIn; lodash.groupBy = groupBy; lodash.initial = initial; lodash.intersection = intersection; lodash.intersectionBy = intersectionBy; lodash.intersectionWith = intersectionWith; lodash.invert = invert; lodash.invertBy = invertBy; lodash.invokeMap = invokeMap; lodash.iteratee = iteratee; lodash.keyBy = keyBy; lodash.keys = keys; lodash.keysIn = keysIn; lodash.map = map; lodash.mapKeys = mapKeys; lodash.mapValues = mapValues; lodash.matches = matches; lodash.matchesProperty = matchesProperty; lodash.memoize = memoize; lodash.merge = merge; lodash.mergeWith = mergeWith; lodash.method = method; lodash.methodOf = methodOf; lodash.mixin = mixin; lodash.negate = negate; lodash.nthArg = nthArg; lodash.omit = omit; lodash.omitBy = omitBy; lodash.once = once; lodash.orderBy = orderBy; lodash.over = over; lodash.overArgs = overArgs; lodash.overEvery = overEvery; lodash.overSome = overSome; lodash.partial = partial; lodash.partialRight = partialRight; lodash.partition = partition; lodash.pick = pick; lodash.pickBy = pickBy; lodash.property = property; lodash.propertyOf = propertyOf; lodash.pull = pull; lodash.pullAll = pullAll; lodash.pullAllBy = pullAllBy; lodash.pullAllWith = pullAllWith; lodash.pullAt = pullAt; lodash.range = range; lodash.rangeRight = rangeRight; lodash.rearg = rearg; lodash.reject = reject; lodash.remove = remove; lodash.rest = rest; lodash.reverse = reverse; lodash.sampleSize = sampleSize; lodash.set = set; lodash.setWith = setWith; lodash.shuffle = shuffle; lodash.slice = slice; lodash.sortBy = sortBy; lodash.sortedUniq = sortedUniq; lodash.sortedUniqBy = sortedUniqBy; lodash.split = split; lodash.spread = spread; lodash.tail = tail; lodash.take = take; lodash.takeRight = takeRight; lodash.takeRightWhile = takeRightWhile; lodash.takeWhile = takeWhile; lodash.tap = tap; lodash.throttle = throttle; lodash.thru = thru; lodash.toArray = toArray; lodash.toPairs = toPairs; lodash.toPairsIn = toPairsIn; lodash.toPath = toPath; lodash.toPlainObject = toPlainObject; lodash.transform = transform; lodash.unary = unary; lodash.union = union; lodash.unionBy = unionBy; lodash.unionWith = unionWith; lodash.uniq = uniq; lodash.uniqBy = uniqBy; lodash.uniqWith = uniqWith; lodash.unset = unset; lodash.unzip = unzip; lodash.unzipWith = unzipWith; lodash.update = update; lodash.updateWith = updateWith; lodash.values = values; lodash.valuesIn = valuesIn; lodash.without = without; lodash.words = words; lodash.wrap = wrap; lodash.xor = xor; lodash.xorBy = xorBy; lodash.xorWith = xorWith; lodash.zip = zip; lodash.zipObject = zipObject; lodash.zipObjectDeep = zipObjectDeep; lodash.zipWith = zipWith; // Add aliases. lodash.entries = toPairs; lodash.entriesIn = toPairsIn; lodash.extend = assignIn; lodash.extendWith = assignInWith; // Add methods to `lodash.prototype`. mixin(lodash, lodash); /*------------------------------------------------------------------------*/ // Add methods that return unwrapped values in chain sequences. lodash.add = add; lodash.attempt = attempt; lodash.camelCase = camelCase; lodash.capitalize = capitalize; lodash.ceil = ceil; lodash.clamp = clamp; lodash.clone = clone; lodash.cloneDeep = cloneDeep; lodash.cloneDeepWith = cloneDeepWith; lodash.cloneWith = cloneWith; lodash.conformsTo = conformsTo; lodash.deburr = deburr; lodash.defaultTo = defaultTo; lodash.divide = divide; lodash.endsWith = endsWith; lodash.eq = eq; lodash.escape = escape; lodash.escapeRegExp = escapeRegExp; lodash.every = every; lodash.find = find; lodash.findIndex = findIndex; lodash.findKey = findKey; lodash.findLast = findLast; lodash.findLastIndex = findLastIndex; lodash.findLastKey = findLastKey; lodash.floor = floor; lodash.forEach = forEach; lodash.forEachRight = forEachRight; lodash.forIn = forIn; lodash.forInRight = forInRight; lodash.forOwn = forOwn; lodash.forOwnRight = forOwnRight; lodash.get = get; lodash.gt = gt; lodash.gte = gte; lodash.has = has; lodash.hasIn = hasIn; lodash.head = head; lodash.identity = identity; lodash.includes = includes; lodash.indexOf = indexOf; lodash.inRange = inRange; lodash.invoke = invoke; lodash.isArguments = isArguments; lodash.isArray = isArray; lodash.isArrayBuffer = isArrayBuffer; lodash.isArrayLike = isArrayLike; lodash.isArrayLikeObject = isArrayLikeObject; lodash.isBoolean = isBoolean; lodash.isBuffer = isBuffer; lodash.isDate = isDate; lodash.isElement = isElement; lodash.isEmpty = isEmpty; lodash.isEqual = isEqual; lodash.isEqualWith = isEqualWith; lodash.isError = isError; lodash.isFinite = isFinite; lodash.isFunction = isFunction; lodash.isInteger = isInteger; lodash.isLength = isLength; lodash.isMap = isMap; lodash.isMatch = isMatch; lodash.isMatchWith = isMatchWith; lodash.isNaN = isNaN; lodash.isNative = isNative; lodash.isNil = isNil; lodash.isNull = isNull; lodash.isNumber = isNumber; lodash.isObject = isObject; lodash.isObjectLike = isObjectLike; lodash.isPlainObject = isPlainObject; lodash.isRegExp = isRegExp; lodash.isSafeInteger = isSafeInteger; lodash.isSet = isSet; lodash.isString = isString; lodash.isSymbol = isSymbol; lodash.isTypedArray = isTypedArray; lodash.isUndefined = isUndefined; lodash.isWeakMap = isWeakMap; lodash.isWeakSet = isWeakSet; lodash.join = join; lodash.kebabCase = kebabCase; lodash.last = last; lodash.lastIndexOf = lastIndexOf; lodash.lowerCase = lowerCase; lodash.lowerFirst = lowerFirst; lodash.lt = lt; lodash.lte = lte; lodash.max = max; lodash.maxBy = maxBy; lodash.mean = mean; lodash.meanBy = meanBy; lodash.min = min; lodash.minBy = minBy; lodash.stubArray = stubArray; lodash.stubFalse = stubFalse; lodash.stubObject = stubObject; lodash.stubString = stubString; lodash.stubTrue = stubTrue; lodash.multiply = multiply; lodash.nth = nth; lodash.noConflict = noConflict; lodash.noop = noop; lodash.now = now; lodash.pad = pad; lodash.padEnd = padEnd; lodash.padStart = padStart; lodash.parseInt = parseInt; lodash.random = random; lodash.reduce = reduce; lodash.reduceRight = reduceRight; lodash.repeat = repeat; lodash.replace = replace; lodash.result = result; lodash.round = round; lodash.runInContext = runInContext; lodash.sample = sample; lodash.size = size; lodash.snakeCase = snakeCase; lodash.some = some; lodash.sortedIndex = sortedIndex; lodash.sortedIndexBy = sortedIndexBy; lodash.sortedIndexOf = sortedIndexOf; lodash.sortedLastIndex = sortedLastIndex; lodash.sortedLastIndexBy = sortedLastIndexBy; lodash.sortedLastIndexOf = sortedLastIndexOf; lodash.startCase = startCase; lodash.startsWith = startsWith; lodash.subtract = subtract; lodash.sum = sum; lodash.sumBy = sumBy; lodash.template = template; lodash.times = times; lodash.toFinite = toFinite; lodash.toInteger = toInteger; lodash.toLength = toLength; lodash.toLower = toLower; lodash.toNumber = toNumber; lodash.toSafeInteger = toSafeInteger; lodash.toString = toString; lodash.toUpper = toUpper; lodash.trim = trim; lodash.trimEnd = trimEnd; lodash.trimStart = trimStart; lodash.truncate = truncate; lodash.unescape = unescape; lodash.uniqueId = uniqueId; lodash.upperCase = upperCase; lodash.upperFirst = upperFirst; // Add aliases. lodash.each = forEach; lodash.eachRight = forEachRight; lodash.first = head; mixin(lodash, (function() { var source = {}; baseForOwn(lodash, function(func, methodName) { if (!hasOwnProperty.call(lodash.prototype, methodName)) { source[methodName] = func; } }); return source; }()), { 'chain': false }); /*------------------------------------------------------------------------*/ /** * The semantic version number. * * @static * @memberOf _ * @type {string} */ lodash.VERSION = VERSION; // Assign default placeholders. arrayEach(['bind', 'bindKey', 'curry', 'curryRight', 'partial', 'partialRight'], function(methodName) { lodash[methodName].placeholder = lodash; }); // Add `LazyWrapper` methods for `_.drop` and `_.take` variants. arrayEach(['drop', 'take'], function(methodName, index) { LazyWrapper.prototype[methodName] = function(n) { n = n === undefined ? 1 : nativeMax(toInteger(n), 0); var result = (this.__filtered__ && !index) ? new LazyWrapper(this) : this.clone(); if (result.__filtered__) { result.__takeCount__ = nativeMin(n, result.__takeCount__); } else { result.__views__.push({ 'size': nativeMin(n, MAX_ARRAY_LENGTH), 'type': methodName + (result.__dir__ < 0 ? 'Right' : '') }); } return result; }; LazyWrapper.prototype[methodName + 'Right'] = function(n) { return this.reverse()[methodName](n).reverse(); }; }); // Add `LazyWrapper` methods that accept an `iteratee` value. arrayEach(['filter', 'map', 'takeWhile'], function(methodName, index) { var type = index + 1, isFilter = type == LAZY_FILTER_FLAG || type == LAZY_WHILE_FLAG; LazyWrapper.prototype[methodName] = function(iteratee) { var result = this.clone(); result.__iteratees__.push({ 'iteratee': getIteratee(iteratee, 3), 'type': type }); result.__filtered__ = result.__filtered__ || isFilter; return result; }; }); // Add `LazyWrapper` methods for `_.head` and `_.last`. arrayEach(['head', 'last'], function(methodName, index) { var takeName = 'take' + (index ? 'Right' : ''); LazyWrapper.prototype[methodName] = function() { return this[takeName](1).value()[0]; }; }); // Add `LazyWrapper` methods for `_.initial` and `_.tail`. arrayEach(['initial', 'tail'], function(methodName, index) { var dropName = 'drop' + (index ? '' : 'Right'); LazyWrapper.prototype[methodName] = function() { return this.__filtered__ ? new LazyWrapper(this) : this[dropName](1); }; }); LazyWrapper.prototype.compact = function() { return this.filter(identity); }; LazyWrapper.prototype.find = function(predicate) { return this.filter(predicate).head(); }; LazyWrapper.prototype.findLast = function(predicate) { return this.reverse().find(predicate); }; LazyWrapper.prototype.invokeMap = baseRest(function(path, args) { if (typeof path == 'function') { return new LazyWrapper(this); } return this.map(function(value) { return baseInvoke(value, path, args); }); }); LazyWrapper.prototype.reject = function(predicate) { return this.filter(negate(getIteratee(predicate))); }; LazyWrapper.prototype.slice = function(start, end) { start = toInteger(start); var result = this; if (result.__filtered__ && (start > 0 || end < 0)) { return new LazyWrapper(result); } if (start < 0) { result = result.takeRight(-start); } else if (start) { result = result.drop(start); } if (end !== undefined) { end = toInteger(end); result = end < 0 ? result.dropRight(-end) : result.take(end - start); } return result; }; LazyWrapper.prototype.takeRightWhile = function(predicate) { return this.reverse().takeWhile(predicate).reverse(); }; LazyWrapper.prototype.toArray = function() { return this.take(MAX_ARRAY_LENGTH); }; // Add `LazyWrapper` methods to `lodash.prototype`. baseForOwn(LazyWrapper.prototype, function(func, methodName) { var checkIteratee = /^(?:filter|find|map|reject)|While$/.test(methodName), isTaker = /^(?:head|last)$/.test(methodName), lodashFunc = lodash[isTaker ? ('take' + (methodName == 'last' ? 'Right' : '')) : methodName], retUnwrapped = isTaker || /^find/.test(methodName); if (!lodashFunc) { return; } lodash.prototype[methodName] = function() { var value = this.__wrapped__, args = isTaker ? [1] : arguments, isLazy = value instanceof LazyWrapper, iteratee = args[0], useLazy = isLazy || isArray(value); var interceptor = function(value) { var result = lodashFunc.apply(lodash, arrayPush([value], args)); return (isTaker && chainAll) ? result[0] : result; }; if (useLazy && checkIteratee && typeof iteratee == 'function' && iteratee.length != 1) { // Avoid lazy use if the iteratee has a "length" value other than `1`. isLazy = useLazy = false; } var chainAll = this.__chain__, isHybrid = !!this.__actions__.length, isUnwrapped = retUnwrapped && !chainAll, onlyLazy = isLazy && !isHybrid; if (!retUnwrapped && useLazy) { value = onlyLazy ? value : new LazyWrapper(this); var result = func.apply(value, args); result.__actions__.push({ 'func': thru, 'args': [interceptor], 'thisArg': undefined }); return new LodashWrapper(result, chainAll); } if (isUnwrapped && onlyLazy) { return func.apply(this, args); } result = this.thru(interceptor); return isUnwrapped ? (isTaker ? result.value()[0] : result.value()) : result; }; }); // Add `Array` methods to `lodash.prototype`. arrayEach(['pop', 'push', 'shift', 'sort', 'splice', 'unshift'], function(methodName) { var func = arrayProto[methodName], chainName = /^(?:push|sort|unshift)$/.test(methodName) ? 'tap' : 'thru', retUnwrapped = /^(?:pop|shift)$/.test(methodName); lodash.prototype[methodName] = function() { var args = arguments; if (retUnwrapped && !this.__chain__) { var value = this.value(); return func.apply(isArray(value) ? value : [], args); } return this[chainName](function(value) { return func.apply(isArray(value) ? value : [], args); }); }; }); // Map minified method names to their real names. baseForOwn(LazyWrapper.prototype, function(func, methodName) { var lodashFunc = lodash[methodName]; if (lodashFunc) { var key = (lodashFunc.name + ''), names = realNames[key] || (realNames[key] = []); names.push({ 'name': methodName, 'func': lodashFunc }); } }); realNames[createHybrid(undefined, WRAP_BIND_KEY_FLAG).name] = [{ 'name': 'wrapper', 'func': undefined }]; // Add methods to `LazyWrapper`. LazyWrapper.prototype.clone = lazyClone; LazyWrapper.prototype.reverse = lazyReverse; LazyWrapper.prototype.value = lazyValue; // Add chain sequence methods to the `lodash` wrapper. lodash.prototype.at = wrapperAt; lodash.prototype.chain = wrapperChain; lodash.prototype.commit = wrapperCommit; lodash.prototype.next = wrapperNext; lodash.prototype.plant = wrapperPlant; lodash.prototype.reverse = wrapperReverse; lodash.prototype.toJSON = lodash.prototype.valueOf = lodash.prototype.value = wrapperValue; // Add lazy aliases. lodash.prototype.first = lodash.prototype.head; if (symIterator) { lodash.prototype[symIterator] = wrapperToIterator; } return lodash; }); /*--------------------------------------------------------------------------*/ // Export lodash. var _ = runInContext(); // Some AMD build optimizers, like r.js, check for condition patterns like: if (true) { // Expose Lodash on the global object to prevent errors when Lodash is // loaded by a script tag in the presence of an AMD loader. // See http://requirejs.org/docs/errors.html#mismatch for more details. // Use `_.noConflict` to remove Lodash from the global object. root._ = _; // Define as an anonymous module so, through path mapping, it can be // referenced as the "underscore" module. !(__WEBPACK_AMD_DEFINE_RESULT__ = function() { return _; }.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__)); } // Check for `exports` after `define` in case a build optimizer adds it. else if (freeModule) { // Export for Node.js. (freeModule.exports = _)._ = _; // Export for CommonJS support. freeExports._ = _; } else { // Export to the global object. root._ = _; } }.call(this)); /* WEBPACK VAR INJECTION */}.call(exports, (function() { return this; }()), __webpack_require__(5)(module))) /***/ }), /* 5 */ /***/ (function(module, exports) { module.exports = function(module) { if(!module.webpackPolyfill) { module.deprecate = function() {}; module.paths = []; // module.parent = undefined by default module.children = []; module.webpackPolyfill = 1; } return module; } /***/ }), /* 6 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; Object.defineProperty(exports, "__esModule", { value: true }); var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }(); var _d2 = __webpack_require__(2); var _d3 = _interopRequireDefault(_d2); var _lodash = __webpack_require__(4); function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } var PredictProba = function () { // svg: d3 object with the svg in question // class_names: array of class names // predict_probas: array of prediction probabilities function PredictProba(svg, class_names, predict_probas) { var title = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : 'Prediction probabilities'; _classCallCheck(this, PredictProba); var width = parseInt(svg.style('width')); this.names = class_names; this.names.push('Other'); if (class_names.length < 10) { this.colors = _d3.default.scale.category10().domain(this.names); this.colors_i = _d3.default.scale.category10().domain((0, _lodash.range)(this.names.length)); } else { this.colors = _d3.default.scale.category20().domain(this.names); this.colors_i = _d3.default.scale.category20().domain((0, _lodash.range)(this.names.length)); } var _map_classes = this.map_classes(this.names, predict_probas), _map_classes2 = _slicedToArray(_map_classes, 2), names = _map_classes2[0], data = _map_classes2[1]; var bar_x = width - 125; var class_names_width = bar_x; var bar_width = width - bar_x - 32; var x_scale = _d3.default.scale.linear().range([0, bar_width]); var bar_height = 17; var space_between_bars = 5; var bar_yshift = title === '' ? 0 : 35; var n_bars = Math.min(5, data.length); this.svg_height = n_bars * (bar_height + space_between_bars) + bar_yshift; svg.style('height', this.svg_height + 'px'); var this_object = this; if (title !== '') { svg.append('text').text(title).attr('x', 20).attr('y', 20); } var bar_y = function bar_y(i) { return (bar_height + space_between_bars) * i + bar_yshift; }; var bar = svg.append("g"); var _iteratorNormalCompletion = true; var _didIteratorError = false; var _iteratorError = undefined; try { for (var _iterator = (0, _lodash.range)(data.length)[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { var i = _step.value; var color = this.colors(names[i]); if (names[i] == 'Other' && this.names.length > 20) { color = '#5F9EA0'; } var rect = bar.append("rect"); rect.attr("x", bar_x).attr("y", bar_y(i)).attr("height", bar_height).attr("width", x_scale(data[i])).style("fill", color); bar.append("rect").attr("x", bar_x).attr("y", bar_y(i)).attr("height", bar_height).attr("width", bar_width - 1).attr("fill-opacity", 0).attr("stroke", "black"); var text = bar.append("text"); text.classed("prob_text", true); text.attr("y", bar_y(i) + bar_height - 3).attr("fill", "black").style("font", "14px tahoma, sans-serif"); text = bar.append("text"); text.attr("x", bar_x + x_scale(data[i]) + 5).attr("y", bar_y(i) + bar_height - 3).attr("fill", "black").style("font", "14px tahoma, sans-serif").text(data[i].toFixed(2)); text = bar.append("text"); text.attr("x", bar_x - 10).attr("y", bar_y(i) + bar_height - 3).attr("fill", "black").attr("text-anchor", "end").style("font", "14px tahoma, sans-serif").text(names[i]); while (text.node().getBBox()['width'] + 1 > class_names_width - 10) { // TODO: ta mostrando só dois, e talvez quando hover mostrar o texto // todo var cur_text = text.text().slice(0, text.text().length - 5); text.text(cur_text + '...'); if (cur_text === '') { break; } } } } catch (err) { _didIteratorError = true; _iteratorError = err; } finally { try { if (!_iteratorNormalCompletion && _iterator.return) { _iterator.return(); } } finally { if (_didIteratorError) { throw _iteratorError; } } } } PredictProba.prototype.map_classes = function map_classes(class_names, predict_proba) { if (class_names.length <= 6) { return [class_names, predict_proba]; } var class_dict = (0, _lodash.range)(predict_proba.length).map(function (i) { return { 'name': class_names[i], 'prob': predict_proba[i], 'i': i }; }); var sorted = (0, _lodash.sortBy)(class_dict, function (d) { return -d.prob; }); var other = new Set(); (0, _lodash.range)(4, sorted.length).map(function (d) { return other.add(sorted[d].name); }); var other_prob = 0; var ret_probs = []; var ret_names = []; var _iteratorNormalCompletion2 = true; var _didIteratorError2 = false; var _iteratorError2 = undefined; try { for (var _iterator2 = (0, _lodash.range)(sorted.length)[Symbol.iterator](), _step2; !(_iteratorNormalCompletion2 = (_step2 = _iterator2.next()).done); _iteratorNormalCompletion2 = true) { var d = _step2.value; if (other.has(sorted[d].name)) { other_prob += sorted[d].prob; } else { ret_probs.push(sorted[d].prob); ret_names.push(sorted[d].name); } } } catch (err) { _didIteratorError2 = true; _iteratorError2 = err; } finally { try { if (!_iteratorNormalCompletion2 && _iterator2.return) { _iterator2.return(); } } finally { if (_didIteratorError2) { throw _iteratorError2; } } } ; ret_names.push("Other"); ret_probs.push(other_prob); return [ret_names, ret_probs]; }; return PredictProba; }(); exports.default = PredictProba; /***/ }), /* 7 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; Object.defineProperty(exports, "__esModule", { value: true }); var _d = __webpack_require__(2); var _d2 = _interopRequireDefault(_d); var _lodash = __webpack_require__(4); function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } var PredictedValue = // svg: d3 object with the svg in question // class_names: array of class names // predict_probas: array of prediction probabilities function PredictedValue(svg, predicted_value, min_value, max_value) { var title = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : 'Predicted value'; var log_coords = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : false; _classCallCheck(this, PredictedValue); if (min_value == max_value) { var width_proportion = 1.0; } else { var width_proportion = (predicted_value - min_value) / (max_value - min_value); } var width = parseInt(svg.style('width')); this.color = _d2.default.scale.category10(); this.color('predicted_value'); // + 2 is due to it being a float var num_digits = Math.floor(Math.max(Math.log10(min_value), Math.log10(max_value))) + 2; num_digits = Math.max(num_digits, 3); var corner_width = 12 * num_digits; var corner_padding = 5.5 * num_digits; var bar_x = corner_width + corner_padding; var bar_width = width - corner_width * 2 - corner_padding * 2; var x_scale = _d2.default.scale.linear().range([0, bar_width]); var bar_height = 17; var bar_yshift = title === '' ? 0 : 35; var n_bars = 1; var this_object = this; if (title !== '') { svg.append('text').text(title).attr('x', 20).attr('y', 20); } var bar_y = bar_yshift; var bar = svg.append("g"); //filled in bar representing predicted value in range var rect = bar.append("rect"); rect.attr("x", bar_x).attr("y", bar_y).attr("height", bar_height).attr("width", x_scale(width_proportion)).style("fill", this.color); //empty box representing range bar.append("rect").attr("x", bar_x).attr("y", bar_y).attr("height", bar_height).attr("width", x_scale(1)).attr("fill-opacity", 0).attr("stroke", "black"); var text = bar.append("text"); text.classed("prob_text", true); text.attr("y", bar_y + bar_height - 3).attr("fill", "black").style("font", "14px tahoma, sans-serif"); //text for min value text = bar.append("text"); text.attr("x", bar_x - corner_padding).attr("y", bar_y + bar_height - 3).attr("fill", "black").attr("text-anchor", "end").style("font", "14px tahoma, sans-serif").text(min_value.toFixed(2)); //text for range min annotation var v_adjust_min_value_annotation = text.node().getBBox().height; text = bar.append("text"); text.attr("x", bar_x - corner_padding).attr("y", bar_y + bar_height - 3 + v_adjust_min_value_annotation).attr("fill", "black").attr("text-anchor", "end").style("font", "14px tahoma, sans-serif").text("(min)"); //text for predicted value // console.log('bar height: ' + bar_height) text = bar.append("text"); text.text(predicted_value.toFixed(2)); // let h_adjust_predicted_value_text = text.node().getBBox().width / 2; var v_adjust_predicted_value_text = text.node().getBBox().height; text.attr("x", bar_x + x_scale(width_proportion)).attr("y", bar_y + bar_height + v_adjust_predicted_value_text).attr("fill", "black").attr("text-anchor", "middle").style("font", "14px tahoma, sans-serif"); //text for max value text = bar.append("text"); text.text(max_value.toFixed(2)); // let h_adjust = text.node().getBBox().width; text.attr("x", bar_x + bar_width + corner_padding).attr("y", bar_y + bar_height - 3).attr("fill", "black").attr("text-anchor", "begin").style("font", "14px tahoma, sans-serif"); //text for range max annotation var v_adjust_max_value_annotation = text.node().getBBox().height; text = bar.append("text"); text.attr("x", bar_x + bar_width + corner_padding).attr("y", bar_y + bar_height - 3 + v_adjust_min_value_annotation).attr("fill", "black").attr("text-anchor", "begin").style("font", "14px tahoma, sans-serif").text("(max)"); //readjust svg size // let svg_width = width + 1 * h_adjust; // svg.style('width', svg_width + 'px'); this.svg_height = n_bars * bar_height + bar_yshift + 2 * text.node().getBBox().height + 10; svg.style('height', this.svg_height + 'px'); if (log_coords) { console.log("svg width: " + svg_width); console.log("svg height: " + this.svg_height); console.log("bar_y: " + bar_y); console.log("bar_x: " + bar_x); console.log("Min value: " + min_value); console.log("Max value: " + max_value); console.log("Pred value: " + predicted_value); } }; exports.default = PredictedValue; /***/ }), /* 8 */ /***/ (function(module, exports, __webpack_require__) { /* WEBPACK VAR INJECTION */(function(global) {"use strict"; __webpack_require__(9); __webpack_require__(300); __webpack_require__(301); if (global._babelPolyfill) { throw new Error("only one instance of babel-polyfill is allowed"); } global._babelPolyfill = true; var DEFINE_PROPERTY = "defineProperty"; function define(O, key, value) { O[key] || Object[DEFINE_PROPERTY](O, key, { writable: true, configurable: true, value: value }); } define(String.prototype, "padLeft", "".padStart); define(String.prototype, "padRight", "".padEnd); "pop,reverse,shift,keys,values,entries,indexOf,every,some,forEach,map,filter,find,findIndex,includes,join,slice,concat,push,splice,unshift,sort,lastIndexOf,reduce,reduceRight,copyWithin,fill".split(",").forEach(function (key) { [][key] && define(Array, key, Function.call.bind([][key])); }); /* WEBPACK VAR INJECTION */}.call(exports, (function() { return this; }()))) /***/ }), /* 9 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(10); __webpack_require__(59); __webpack_require__(60); __webpack_require__(61); __webpack_require__(62); __webpack_require__(64); __webpack_require__(67); __webpack_require__(68); __webpack_require__(69); __webpack_require__(70); __webpack_require__(71); __webpack_require__(72); __webpack_require__(73); __webpack_require__(74); __webpack_require__(75); __webpack_require__(77); __webpack_require__(79); __webpack_require__(81); __webpack_require__(83); 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module.exports = __webpack_require__(16); /***/ }), /* 10 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // ECMAScript 6 symbols shim var global = __webpack_require__(11) , has = __webpack_require__(12) , DESCRIPTORS = __webpack_require__(13) , $export = __webpack_require__(15) , redefine = __webpack_require__(25) , META = __webpack_require__(29).KEY , $fails = __webpack_require__(14) , shared = __webpack_require__(30) , setToStringTag = __webpack_require__(31) , uid = __webpack_require__(26) , wks = __webpack_require__(32) , wksExt = __webpack_require__(33) , wksDefine = __webpack_require__(34) , keyOf = __webpack_require__(36) , enumKeys = __webpack_require__(49) , isArray = __webpack_require__(52) , anObject = __webpack_require__(19) , toIObject = __webpack_require__(39) , toPrimitive = __webpack_require__(23) , createDesc = __webpack_require__(24) , _create = __webpack_require__(53) , gOPNExt = __webpack_require__(56) , $GOPD = __webpack_require__(58) , $DP = __webpack_require__(18) , $keys = __webpack_require__(37) , gOPD = $GOPD.f , dP = $DP.f , gOPN = gOPNExt.f , $Symbol = global.Symbol , $JSON = global.JSON , _stringify = $JSON && $JSON.stringify , PROTOTYPE = 'prototype' , HIDDEN = wks('_hidden') , TO_PRIMITIVE = wks('toPrimitive') , isEnum = {}.propertyIsEnumerable , SymbolRegistry = shared('symbol-registry') , AllSymbols = shared('symbols') , OPSymbols = shared('op-symbols') , ObjectProto = Object[PROTOTYPE] , USE_NATIVE = typeof $Symbol == 'function' , QObject = global.QObject; // Don't use setters in Qt Script, https://github.com/zloirock/core-js/issues/173 var setter = !QObject || !QObject[PROTOTYPE] || !QObject[PROTOTYPE].findChild; // fallback for old Android, https://code.google.com/p/v8/issues/detail?id=687 var setSymbolDesc = DESCRIPTORS && $fails(function(){ return _create(dP({}, 'a', { get: function(){ return dP(this, 'a', {value: 7}).a; } })).a != 7; }) ? function(it, key, D){ var protoDesc = gOPD(ObjectProto, key); if(protoDesc)delete ObjectProto[key]; dP(it, key, D); if(protoDesc && it !== ObjectProto)dP(ObjectProto, key, protoDesc); } : dP; var wrap = function(tag){ var sym = AllSymbols[tag] = _create($Symbol[PROTOTYPE]); sym._k = tag; return sym; }; var isSymbol = USE_NATIVE && typeof $Symbol.iterator == 'symbol' ? function(it){ return typeof it == 'symbol'; } : function(it){ return it instanceof $Symbol; }; var $defineProperty = function defineProperty(it, key, D){ if(it === ObjectProto)$defineProperty(OPSymbols, key, D); anObject(it); key = toPrimitive(key, true); anObject(D); if(has(AllSymbols, key)){ if(!D.enumerable){ if(!has(it, HIDDEN))dP(it, HIDDEN, createDesc(1, {})); it[HIDDEN][key] = true; } else { if(has(it, HIDDEN) && it[HIDDEN][key])it[HIDDEN][key] = false; D = _create(D, {enumerable: createDesc(0, false)}); } return setSymbolDesc(it, key, D); } return dP(it, key, D); }; var $defineProperties = function defineProperties(it, P){ anObject(it); var keys = enumKeys(P = toIObject(P)) , i = 0 , l = keys.length , key; while(l > i)$defineProperty(it, key = keys[i++], P[key]); return it; }; var $create = function create(it, P){ return P === undefined ? _create(it) : $defineProperties(_create(it), P); }; var $propertyIsEnumerable = function propertyIsEnumerable(key){ var E = isEnum.call(this, key = toPrimitive(key, true)); if(this === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key))return false; return E || !has(this, key) || !has(AllSymbols, key) || has(this, HIDDEN) && this[HIDDEN][key] ? E : true; }; var $getOwnPropertyDescriptor = function getOwnPropertyDescriptor(it, key){ it = toIObject(it); key = toPrimitive(key, true); if(it === ObjectProto && has(AllSymbols, key) && !has(OPSymbols, key))return; var D = gOPD(it, key); if(D && has(AllSymbols, key) && !(has(it, HIDDEN) && it[HIDDEN][key]))D.enumerable = true; return D; }; var $getOwnPropertyNames = function getOwnPropertyNames(it){ var names = gOPN(toIObject(it)) , result = [] , i = 0 , key; while(names.length > i){ if(!has(AllSymbols, key = names[i++]) && key != HIDDEN && key != META)result.push(key); } return result; }; var $getOwnPropertySymbols = function getOwnPropertySymbols(it){ var IS_OP = it === ObjectProto , names = gOPN(IS_OP ? OPSymbols : toIObject(it)) , result = [] , i = 0 , key; while(names.length > i){ if(has(AllSymbols, key = names[i++]) && (IS_OP ? has(ObjectProto, key) : true))result.push(AllSymbols[key]); } return result; }; // 19.4.1.1 Symbol([description]) if(!USE_NATIVE){ $Symbol = function Symbol(){ if(this instanceof $Symbol)throw TypeError('Symbol is not a constructor!'); var tag = uid(arguments.length > 0 ? arguments[0] : undefined); var $set = function(value){ if(this === ObjectProto)$set.call(OPSymbols, value); if(has(this, HIDDEN) && has(this[HIDDEN], tag))this[HIDDEN][tag] = false; setSymbolDesc(this, tag, createDesc(1, value)); }; if(DESCRIPTORS && setter)setSymbolDesc(ObjectProto, tag, {configurable: true, set: $set}); return wrap(tag); }; redefine($Symbol[PROTOTYPE], 'toString', function toString(){ return this._k; }); $GOPD.f = $getOwnPropertyDescriptor; $DP.f = $defineProperty; __webpack_require__(57).f = gOPNExt.f = $getOwnPropertyNames; __webpack_require__(51).f = $propertyIsEnumerable; __webpack_require__(50).f = $getOwnPropertySymbols; if(DESCRIPTORS && !__webpack_require__(35)){ redefine(ObjectProto, 'propertyIsEnumerable', $propertyIsEnumerable, true); } wksExt.f = function(name){ return wrap(wks(name)); } } $export($export.G + $export.W + $export.F * !USE_NATIVE, {Symbol: $Symbol}); for(var symbols = ( // 19.4.2.2, 19.4.2.3, 19.4.2.4, 19.4.2.6, 19.4.2.8, 19.4.2.9, 19.4.2.10, 19.4.2.11, 19.4.2.12, 19.4.2.13, 19.4.2.14 'hasInstance,isConcatSpreadable,iterator,match,replace,search,species,split,toPrimitive,toStringTag,unscopables' ).split(','), i = 0; symbols.length > i; )wks(symbols[i++]); for(var symbols = $keys(wks.store), i = 0; symbols.length > i; )wksDefine(symbols[i++]); $export($export.S + $export.F * !USE_NATIVE, 'Symbol', { // 19.4.2.1 Symbol.for(key) 'for': function(key){ return has(SymbolRegistry, key += '') ? SymbolRegistry[key] : SymbolRegistry[key] = $Symbol(key); }, // 19.4.2.5 Symbol.keyFor(sym) keyFor: function keyFor(key){ if(isSymbol(key))return keyOf(SymbolRegistry, key); throw TypeError(key + ' is not a symbol!'); }, useSetter: function(){ setter = true; }, useSimple: function(){ setter = false; } }); $export($export.S + $export.F * !USE_NATIVE, 'Object', { // 19.1.2.2 Object.create(O [, Properties]) create: $create, // 19.1.2.4 Object.defineProperty(O, P, Attributes) defineProperty: $defineProperty, // 19.1.2.3 Object.defineProperties(O, Properties) defineProperties: $defineProperties, // 19.1.2.6 Object.getOwnPropertyDescriptor(O, P) getOwnPropertyDescriptor: $getOwnPropertyDescriptor, // 19.1.2.7 Object.getOwnPropertyNames(O) getOwnPropertyNames: $getOwnPropertyNames, // 19.1.2.8 Object.getOwnPropertySymbols(O) getOwnPropertySymbols: $getOwnPropertySymbols }); // 24.3.2 JSON.stringify(value [, replacer [, space]]) $JSON && $export($export.S + $export.F * (!USE_NATIVE || $fails(function(){ var S = $Symbol(); // MS Edge converts symbol values to JSON as {} // WebKit converts symbol values to JSON as null // V8 throws on boxed symbols return _stringify([S]) != '[null]' || _stringify({a: S}) != '{}' || _stringify(Object(S)) != '{}'; })), 'JSON', { stringify: function stringify(it){ if(it === undefined || isSymbol(it))return; // IE8 returns string on undefined var args = [it] , i = 1 , replacer, $replacer; while(arguments.length > i)args.push(arguments[i++]); replacer = args[1]; if(typeof replacer == 'function')$replacer = replacer; if($replacer || !isArray(replacer))replacer = function(key, value){ if($replacer)value = $replacer.call(this, key, value); if(!isSymbol(value))return value; }; args[1] = replacer; return _stringify.apply($JSON, args); } }); // 19.4.3.4 Symbol.prototype[@@toPrimitive](hint) $Symbol[PROTOTYPE][TO_PRIMITIVE] || __webpack_require__(17)($Symbol[PROTOTYPE], TO_PRIMITIVE, $Symbol[PROTOTYPE].valueOf); // 19.4.3.5 Symbol.prototype[@@toStringTag] setToStringTag($Symbol, 'Symbol'); // 20.2.1.9 Math[@@toStringTag] setToStringTag(Math, 'Math', true); // 24.3.3 JSON[@@toStringTag] setToStringTag(global.JSON, 'JSON', true); /***/ }), /* 11 */ /***/ (function(module, exports) { // https://github.com/zloirock/core-js/issues/86#issuecomment-115759028 var global = module.exports = typeof window != 'undefined' && window.Math == Math ? window : typeof self != 'undefined' && self.Math == Math ? self : Function('return this')(); if(typeof __g == 'number')__g = global; // eslint-disable-line no-undef /***/ }), /* 12 */ /***/ (function(module, exports) { var hasOwnProperty = {}.hasOwnProperty; module.exports = function(it, key){ return hasOwnProperty.call(it, key); }; /***/ }), /* 13 */ /***/ (function(module, exports, __webpack_require__) { // Thank's IE8 for his funny defineProperty module.exports = !__webpack_require__(14)(function(){ return Object.defineProperty({}, 'a', {get: function(){ return 7; }}).a != 7; }); /***/ }), /* 14 */ /***/ (function(module, exports) { module.exports = function(exec){ try { return !!exec(); } catch(e){ return true; } }; /***/ }), /* 15 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , core = __webpack_require__(16) , hide = __webpack_require__(17) , redefine = __webpack_require__(25) , ctx = __webpack_require__(27) , PROTOTYPE = 'prototype'; var $export = function(type, name, source){ var IS_FORCED = type & $export.F , IS_GLOBAL = type & $export.G , IS_STATIC = type & $export.S , IS_PROTO = type & $export.P , IS_BIND = type & $export.B , target = IS_GLOBAL ? global : IS_STATIC ? global[name] || (global[name] = {}) : (global[name] || {})[PROTOTYPE] , exports = IS_GLOBAL ? core : core[name] || (core[name] = {}) , expProto = exports[PROTOTYPE] || (exports[PROTOTYPE] = {}) , key, own, out, exp; if(IS_GLOBAL)source = name; for(key in source){ // contains in native own = !IS_FORCED && target && target[key] !== undefined; // export native or passed out = (own ? target : source)[key]; // bind timers to global for call from export context exp = IS_BIND && own ? ctx(out, global) : IS_PROTO && typeof out == 'function' ? ctx(Function.call, out) : out; // extend global if(target)redefine(target, key, out, type & $export.U); // export if(exports[key] != out)hide(exports, key, exp); if(IS_PROTO && expProto[key] != out)expProto[key] = out; } }; global.core = core; // type bitmap $export.F = 1; // forced $export.G = 2; // global $export.S = 4; // static $export.P = 8; // proto $export.B = 16; // bind $export.W = 32; // wrap $export.U = 64; // safe $export.R = 128; // real proto method for `library` module.exports = $export; /***/ }), /* 16 */ /***/ (function(module, exports) { var core = module.exports = {version: '2.4.0'}; if(typeof __e == 'number')__e = core; // eslint-disable-line no-undef /***/ }), /* 17 */ /***/ (function(module, exports, __webpack_require__) { var dP = __webpack_require__(18) , createDesc = __webpack_require__(24); module.exports = __webpack_require__(13) ? function(object, key, value){ return dP.f(object, key, createDesc(1, value)); } : function(object, key, value){ object[key] = value; return object; }; /***/ }), /* 18 */ /***/ (function(module, exports, __webpack_require__) { var anObject = __webpack_require__(19) , IE8_DOM_DEFINE = __webpack_require__(21) , toPrimitive = __webpack_require__(23) , dP = Object.defineProperty; exports.f = __webpack_require__(13) ? Object.defineProperty : function defineProperty(O, P, Attributes){ anObject(O); P = toPrimitive(P, true); anObject(Attributes); if(IE8_DOM_DEFINE)try { return dP(O, P, Attributes); } catch(e){ /* empty */ } if('get' in Attributes || 'set' in Attributes)throw TypeError('Accessors not supported!'); if('value' in Attributes)O[P] = Attributes.value; return O; }; /***/ }), /* 19 */ /***/ (function(module, exports, __webpack_require__) { var isObject = __webpack_require__(20); module.exports = function(it){ if(!isObject(it))throw TypeError(it + ' is not an object!'); return it; }; /***/ }), /* 20 */ /***/ (function(module, exports) { module.exports = function(it){ return typeof it === 'object' ? it !== null : typeof it === 'function'; }; /***/ }), /* 21 */ /***/ (function(module, exports, __webpack_require__) { module.exports = !__webpack_require__(13) && !__webpack_require__(14)(function(){ return Object.defineProperty(__webpack_require__(22)('div'), 'a', {get: function(){ return 7; }}).a != 7; }); /***/ }), /* 22 */ /***/ (function(module, exports, __webpack_require__) { var isObject = __webpack_require__(20) , document = __webpack_require__(11).document // in old IE typeof document.createElement is 'object' , is = isObject(document) && isObject(document.createElement); module.exports = function(it){ return is ? document.createElement(it) : {}; }; /***/ }), /* 23 */ /***/ (function(module, exports, __webpack_require__) { // 7.1.1 ToPrimitive(input [, PreferredType]) var isObject = __webpack_require__(20); // instead of the ES6 spec version, we didn't implement @@toPrimitive case // and the second argument - flag - preferred type is a string module.exports = function(it, S){ if(!isObject(it))return it; var fn, val; if(S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it)))return val; if(typeof (fn = it.valueOf) == 'function' && !isObject(val = fn.call(it)))return val; if(!S && typeof (fn = it.toString) == 'function' && !isObject(val = fn.call(it)))return val; throw TypeError("Can't convert object to primitive value"); }; /***/ }), /* 24 */ /***/ (function(module, exports) { module.exports = function(bitmap, value){ return { enumerable : !(bitmap & 1), configurable: !(bitmap & 2), writable : !(bitmap & 4), value : value }; }; /***/ }), /* 25 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , hide = __webpack_require__(17) , has = __webpack_require__(12) , SRC = __webpack_require__(26)('src') , TO_STRING = 'toString' , $toString = Function[TO_STRING] , TPL = ('' + $toString).split(TO_STRING); __webpack_require__(16).inspectSource = function(it){ return $toString.call(it); }; (module.exports = function(O, key, val, safe){ var isFunction = typeof val == 'function'; if(isFunction)has(val, 'name') || hide(val, 'name', key); if(O[key] === val)return; if(isFunction)has(val, SRC) || hide(val, SRC, O[key] ? '' + O[key] : TPL.join(String(key))); if(O === global){ O[key] = val; } else { if(!safe){ delete O[key]; hide(O, key, val); } else { if(O[key])O[key] = val; else hide(O, key, val); } } // add fake Function#toString for correct work wrapped methods / constructors with methods like LoDash isNative })(Function.prototype, TO_STRING, function toString(){ return typeof this == 'function' && this[SRC] || $toString.call(this); }); /***/ }), /* 26 */ /***/ (function(module, exports) { var id = 0 , px = Math.random(); module.exports = function(key){ return 'Symbol('.concat(key === undefined ? '' : key, ')_', (++id + px).toString(36)); }; /***/ }), /* 27 */ /***/ (function(module, exports, __webpack_require__) { // optional / simple context binding var aFunction = __webpack_require__(28); module.exports = function(fn, that, length){ aFunction(fn); if(that === undefined)return fn; switch(length){ case 1: return function(a){ return fn.call(that, a); }; case 2: return function(a, b){ return fn.call(that, a, b); }; case 3: return function(a, b, c){ return fn.call(that, a, b, c); }; } return function(/* ...args */){ return fn.apply(that, arguments); }; }; /***/ }), /* 28 */ /***/ (function(module, exports) { module.exports = function(it){ if(typeof it != 'function')throw TypeError(it + ' is not a function!'); return it; }; /***/ }), /* 29 */ /***/ (function(module, exports, __webpack_require__) { var META = __webpack_require__(26)('meta') , isObject = __webpack_require__(20) , has = __webpack_require__(12) , setDesc = __webpack_require__(18).f , id = 0; var isExtensible = Object.isExtensible || function(){ return true; }; var FREEZE = !__webpack_require__(14)(function(){ return isExtensible(Object.preventExtensions({})); }); var setMeta = function(it){ setDesc(it, META, {value: { i: 'O' + ++id, // object ID w: {} // weak collections IDs }}); }; var fastKey = function(it, create){ // return primitive with prefix if(!isObject(it))return typeof it == 'symbol' ? it : (typeof it == 'string' ? 'S' : 'P') + it; if(!has(it, META)){ // can't set metadata to uncaught frozen object if(!isExtensible(it))return 'F'; // not necessary to add metadata if(!create)return 'E'; // add missing metadata setMeta(it); // return object ID } return it[META].i; }; var getWeak = function(it, create){ if(!has(it, META)){ // can't set metadata to uncaught frozen object if(!isExtensible(it))return true; // not necessary to add metadata if(!create)return false; // add missing metadata setMeta(it); // return hash weak collections IDs } return it[META].w; }; // add metadata on freeze-family methods calling var onFreeze = function(it){ if(FREEZE && meta.NEED && isExtensible(it) && !has(it, META))setMeta(it); return it; }; var meta = module.exports = { KEY: META, NEED: false, fastKey: fastKey, getWeak: getWeak, onFreeze: onFreeze }; /***/ }), /* 30 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , SHARED = '__core-js_shared__' , store = global[SHARED] || (global[SHARED] = {}); module.exports = function(key){ return store[key] || (store[key] = {}); }; /***/ }), /* 31 */ /***/ (function(module, exports, __webpack_require__) { var def = __webpack_require__(18).f , has = __webpack_require__(12) , TAG = __webpack_require__(32)('toStringTag'); module.exports = function(it, tag, stat){ if(it && !has(it = stat ? it : it.prototype, TAG))def(it, TAG, {configurable: true, value: tag}); }; /***/ }), /* 32 */ /***/ (function(module, exports, __webpack_require__) { var store = __webpack_require__(30)('wks') , uid = __webpack_require__(26) , Symbol = __webpack_require__(11).Symbol , USE_SYMBOL = typeof Symbol == 'function'; var $exports = module.exports = function(name){ return store[name] || (store[name] = USE_SYMBOL && Symbol[name] || (USE_SYMBOL ? Symbol : uid)('Symbol.' + name)); }; $exports.store = store; /***/ }), /* 33 */ /***/ (function(module, exports, __webpack_require__) { exports.f = __webpack_require__(32); /***/ }), /* 34 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , core = __webpack_require__(16) , LIBRARY = __webpack_require__(35) , wksExt = __webpack_require__(33) , defineProperty = __webpack_require__(18).f; module.exports = function(name){ var $Symbol = core.Symbol || (core.Symbol = LIBRARY ? {} : global.Symbol || {}); if(name.charAt(0) != '_' && !(name in $Symbol))defineProperty($Symbol, name, {value: wksExt.f(name)}); }; /***/ }), /* 35 */ /***/ (function(module, exports) { module.exports = false; /***/ }), /* 36 */ /***/ (function(module, exports, __webpack_require__) { var getKeys = __webpack_require__(37) , toIObject = __webpack_require__(39); module.exports = function(object, el){ var O = toIObject(object) , keys = getKeys(O) , length = keys.length , index = 0 , key; while(length > index)if(O[key = keys[index++]] === el)return key; }; /***/ }), /* 37 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.14 / 15.2.3.14 Object.keys(O) var $keys = __webpack_require__(38) , enumBugKeys = __webpack_require__(48); module.exports = Object.keys || function keys(O){ return $keys(O, enumBugKeys); }; /***/ }), /* 38 */ /***/ (function(module, exports, __webpack_require__) { var has = __webpack_require__(12) , toIObject = __webpack_require__(39) , arrayIndexOf = __webpack_require__(43)(false) , IE_PROTO = __webpack_require__(47)('IE_PROTO'); module.exports = function(object, names){ var O = toIObject(object) , i = 0 , result = [] , key; for(key in O)if(key != IE_PROTO)has(O, key) && result.push(key); // Don't enum bug & hidden keys while(names.length > i)if(has(O, key = names[i++])){ ~arrayIndexOf(result, key) || result.push(key); } return result; }; /***/ }), /* 39 */ /***/ (function(module, exports, __webpack_require__) { // to indexed object, toObject with fallback for non-array-like ES3 strings var IObject = __webpack_require__(40) , defined = __webpack_require__(42); module.exports = function(it){ return IObject(defined(it)); }; /***/ }), /* 40 */ /***/ (function(module, exports, __webpack_require__) { // fallback for non-array-like ES3 and non-enumerable old V8 strings var cof = __webpack_require__(41); module.exports = Object('z').propertyIsEnumerable(0) ? Object : function(it){ return cof(it) == 'String' ? it.split('') : Object(it); }; /***/ }), /* 41 */ /***/ (function(module, exports) { var toString = {}.toString; module.exports = function(it){ return toString.call(it).slice(8, -1); }; /***/ }), /* 42 */ /***/ (function(module, exports) { // 7.2.1 RequireObjectCoercible(argument) module.exports = function(it){ if(it == undefined)throw TypeError("Can't call method on " + it); return it; }; /***/ }), /* 43 */ /***/ (function(module, exports, __webpack_require__) { // false -> Array#indexOf // true -> Array#includes var toIObject = __webpack_require__(39) , toLength = __webpack_require__(44) , toIndex = __webpack_require__(46); module.exports = function(IS_INCLUDES){ return function($this, el, fromIndex){ var O = toIObject($this) , length = toLength(O.length) , index = toIndex(fromIndex, length) , value; // Array#includes uses SameValueZero equality algorithm if(IS_INCLUDES && el != el)while(length > index){ value = O[index++]; if(value != value)return true; // Array#toIndex ignores holes, Array#includes - not } else for(;length > index; index++)if(IS_INCLUDES || index in O){ if(O[index] === el)return IS_INCLUDES || index || 0; } return !IS_INCLUDES && -1; }; }; /***/ }), /* 44 */ /***/ (function(module, exports, __webpack_require__) { // 7.1.15 ToLength var toInteger = __webpack_require__(45) , min = Math.min; module.exports = function(it){ return it > 0 ? min(toInteger(it), 0x1fffffffffffff) : 0; // pow(2, 53) - 1 == 9007199254740991 }; /***/ }), /* 45 */ /***/ (function(module, exports) { // 7.1.4 ToInteger var ceil = Math.ceil , floor = Math.floor; module.exports = function(it){ return isNaN(it = +it) ? 0 : (it > 0 ? floor : ceil)(it); }; /***/ }), /* 46 */ /***/ (function(module, exports, __webpack_require__) { var toInteger = __webpack_require__(45) , max = Math.max , min = Math.min; module.exports = function(index, length){ index = toInteger(index); return index < 0 ? max(index + length, 0) : min(index, length); }; /***/ }), /* 47 */ /***/ (function(module, exports, __webpack_require__) { var shared = __webpack_require__(30)('keys') , uid = __webpack_require__(26); module.exports = function(key){ return shared[key] || (shared[key] = uid(key)); }; /***/ }), /* 48 */ /***/ (function(module, exports) { // IE 8- don't enum bug keys module.exports = ( 'constructor,hasOwnProperty,isPrototypeOf,propertyIsEnumerable,toLocaleString,toString,valueOf' ).split(','); /***/ }), /* 49 */ /***/ (function(module, exports, __webpack_require__) { // all enumerable object keys, includes symbols var getKeys = __webpack_require__(37) , gOPS = __webpack_require__(50) , pIE = __webpack_require__(51); module.exports = function(it){ var result = getKeys(it) , getSymbols = gOPS.f; if(getSymbols){ var symbols = getSymbols(it) , isEnum = pIE.f , i = 0 , key; while(symbols.length > i)if(isEnum.call(it, key = symbols[i++]))result.push(key); } return result; }; /***/ }), /* 50 */ /***/ (function(module, exports) { exports.f = Object.getOwnPropertySymbols; /***/ }), /* 51 */ /***/ (function(module, exports) { exports.f = {}.propertyIsEnumerable; /***/ }), /* 52 */ /***/ (function(module, exports, __webpack_require__) { // 7.2.2 IsArray(argument) var cof = __webpack_require__(41); module.exports = Array.isArray || function isArray(arg){ return cof(arg) == 'Array'; }; /***/ }), /* 53 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties]) var anObject = __webpack_require__(19) , dPs = __webpack_require__(54) , enumBugKeys = __webpack_require__(48) , IE_PROTO = __webpack_require__(47)('IE_PROTO') , Empty = function(){ /* empty */ } , PROTOTYPE = 'prototype'; // Create object with fake `null` prototype: use iframe Object with cleared prototype var createDict = function(){ // Thrash, waste and sodomy: IE GC bug var iframe = __webpack_require__(22)('iframe') , i = enumBugKeys.length , lt = '<' , gt = '>' , iframeDocument; iframe.style.display = 'none'; __webpack_require__(55).appendChild(iframe); iframe.src = 'javascript:'; // eslint-disable-line no-script-url // createDict = iframe.contentWindow.Object; // html.removeChild(iframe); iframeDocument = iframe.contentWindow.document; iframeDocument.open(); iframeDocument.write(lt + 'script' + gt + 'document.F=Object' + lt + '/script' + gt); iframeDocument.close(); createDict = iframeDocument.F; while(i--)delete createDict[PROTOTYPE][enumBugKeys[i]]; return createDict(); }; module.exports = Object.create || function create(O, Properties){ var result; if(O !== null){ Empty[PROTOTYPE] = anObject(O); result = new Empty; Empty[PROTOTYPE] = null; // add "__proto__" for Object.getPrototypeOf polyfill result[IE_PROTO] = O; } else result = createDict(); return Properties === undefined ? result : dPs(result, Properties); }; /***/ }), /* 54 */ /***/ (function(module, exports, __webpack_require__) { var dP = __webpack_require__(18) , anObject = __webpack_require__(19) , getKeys = __webpack_require__(37); module.exports = __webpack_require__(13) ? Object.defineProperties : function defineProperties(O, Properties){ anObject(O); var keys = getKeys(Properties) , length = keys.length , i = 0 , P; while(length > i)dP.f(O, P = keys[i++], Properties[P]); return O; }; /***/ }), /* 55 */ /***/ (function(module, exports, __webpack_require__) { module.exports = __webpack_require__(11).document && document.documentElement; /***/ }), /* 56 */ /***/ (function(module, exports, __webpack_require__) { // fallback for IE11 buggy Object.getOwnPropertyNames with iframe and window var toIObject = __webpack_require__(39) , gOPN = __webpack_require__(57).f , toString = {}.toString; var windowNames = typeof window == 'object' && window && Object.getOwnPropertyNames ? Object.getOwnPropertyNames(window) : []; var getWindowNames = function(it){ try { return gOPN(it); } catch(e){ return windowNames.slice(); } }; module.exports.f = function getOwnPropertyNames(it){ return windowNames && toString.call(it) == '[object Window]' ? getWindowNames(it) : gOPN(toIObject(it)); }; /***/ }), /* 57 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.7 / 15.2.3.4 Object.getOwnPropertyNames(O) var $keys = __webpack_require__(38) , hiddenKeys = __webpack_require__(48).concat('length', 'prototype'); exports.f = Object.getOwnPropertyNames || function getOwnPropertyNames(O){ return $keys(O, hiddenKeys); }; /***/ }), /* 58 */ /***/ (function(module, exports, __webpack_require__) { var pIE = __webpack_require__(51) , createDesc = __webpack_require__(24) , toIObject = __webpack_require__(39) , toPrimitive = __webpack_require__(23) , has = __webpack_require__(12) , IE8_DOM_DEFINE = __webpack_require__(21) , gOPD = Object.getOwnPropertyDescriptor; exports.f = __webpack_require__(13) ? gOPD : function getOwnPropertyDescriptor(O, P){ O = toIObject(O); P = toPrimitive(P, true); if(IE8_DOM_DEFINE)try { return gOPD(O, P); } catch(e){ /* empty */ } if(has(O, P))return createDesc(!pIE.f.call(O, P), O[P]); }; /***/ }), /* 59 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) // 19.1.2.2 / 15.2.3.5 Object.create(O [, Properties]) $export($export.S, 'Object', {create: __webpack_require__(53)}); /***/ }), /* 60 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15); // 19.1.2.4 / 15.2.3.6 Object.defineProperty(O, P, Attributes) $export($export.S + $export.F * !__webpack_require__(13), 'Object', {defineProperty: __webpack_require__(18).f}); /***/ }), /* 61 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15); // 19.1.2.3 / 15.2.3.7 Object.defineProperties(O, Properties) $export($export.S + $export.F * !__webpack_require__(13), 'Object', {defineProperties: __webpack_require__(54)}); /***/ }), /* 62 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.6 Object.getOwnPropertyDescriptor(O, P) var toIObject = __webpack_require__(39) , $getOwnPropertyDescriptor = __webpack_require__(58).f; __webpack_require__(63)('getOwnPropertyDescriptor', function(){ return function getOwnPropertyDescriptor(it, key){ return $getOwnPropertyDescriptor(toIObject(it), key); }; }); /***/ }), /* 63 */ /***/ (function(module, exports, __webpack_require__) { // most Object methods by ES6 should accept primitives var $export = __webpack_require__(15) , core = __webpack_require__(16) , fails = __webpack_require__(14); module.exports = function(KEY, exec){ var fn = (core.Object || {})[KEY] || Object[KEY] , exp = {}; exp[KEY] = exec(fn); $export($export.S + $export.F * fails(function(){ fn(1); }), 'Object', exp); }; /***/ }), /* 64 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.9 Object.getPrototypeOf(O) var toObject = __webpack_require__(65) , $getPrototypeOf = __webpack_require__(66); __webpack_require__(63)('getPrototypeOf', function(){ return function getPrototypeOf(it){ return $getPrototypeOf(toObject(it)); }; }); /***/ }), /* 65 */ /***/ (function(module, exports, __webpack_require__) { // 7.1.13 ToObject(argument) var defined = __webpack_require__(42); module.exports = function(it){ return Object(defined(it)); }; /***/ }), /* 66 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.9 / 15.2.3.2 Object.getPrototypeOf(O) var has = __webpack_require__(12) , toObject = __webpack_require__(65) , IE_PROTO = __webpack_require__(47)('IE_PROTO') , ObjectProto = Object.prototype; module.exports = Object.getPrototypeOf || function(O){ O = toObject(O); if(has(O, IE_PROTO))return O[IE_PROTO]; if(typeof O.constructor == 'function' && O instanceof O.constructor){ return O.constructor.prototype; } return O instanceof Object ? ObjectProto : null; }; /***/ }), /* 67 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.14 Object.keys(O) var toObject = __webpack_require__(65) , $keys = __webpack_require__(37); __webpack_require__(63)('keys', function(){ return function keys(it){ return $keys(toObject(it)); }; }); /***/ }), /* 68 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.7 Object.getOwnPropertyNames(O) __webpack_require__(63)('getOwnPropertyNames', function(){ return __webpack_require__(56).f; }); /***/ }), /* 69 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.5 Object.freeze(O) var isObject = __webpack_require__(20) , meta = __webpack_require__(29).onFreeze; __webpack_require__(63)('freeze', function($freeze){ return function freeze(it){ return $freeze && isObject(it) ? $freeze(meta(it)) : it; }; }); /***/ }), /* 70 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.17 Object.seal(O) var isObject = __webpack_require__(20) , meta = __webpack_require__(29).onFreeze; __webpack_require__(63)('seal', function($seal){ return function seal(it){ return $seal && isObject(it) ? $seal(meta(it)) : it; }; }); /***/ }), /* 71 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.15 Object.preventExtensions(O) var isObject = __webpack_require__(20) , meta = __webpack_require__(29).onFreeze; __webpack_require__(63)('preventExtensions', function($preventExtensions){ return function preventExtensions(it){ return $preventExtensions && isObject(it) ? $preventExtensions(meta(it)) : it; }; }); /***/ }), /* 72 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.12 Object.isFrozen(O) var isObject = __webpack_require__(20); __webpack_require__(63)('isFrozen', function($isFrozen){ return function isFrozen(it){ return isObject(it) ? $isFrozen ? $isFrozen(it) : false : true; }; }); /***/ }), /* 73 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.13 Object.isSealed(O) var isObject = __webpack_require__(20); __webpack_require__(63)('isSealed', function($isSealed){ return function isSealed(it){ return isObject(it) ? $isSealed ? $isSealed(it) : false : true; }; }); /***/ }), /* 74 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.2.11 Object.isExtensible(O) var isObject = __webpack_require__(20); __webpack_require__(63)('isExtensible', function($isExtensible){ return function isExtensible(it){ return isObject(it) ? $isExtensible ? $isExtensible(it) : true : false; }; }); /***/ }), /* 75 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.3.1 Object.assign(target, source) var $export = __webpack_require__(15); $export($export.S + $export.F, 'Object', {assign: __webpack_require__(76)}); /***/ }), /* 76 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 19.1.2.1 Object.assign(target, source, ...) var getKeys = __webpack_require__(37) , gOPS = __webpack_require__(50) , pIE = __webpack_require__(51) , toObject = __webpack_require__(65) , IObject = __webpack_require__(40) , $assign = Object.assign; // should work with symbols and should have deterministic property order (V8 bug) module.exports = !$assign || __webpack_require__(14)(function(){ var A = {} , B = {} , S = Symbol() , K = 'abcdefghijklmnopqrst'; A[S] = 7; K.split('').forEach(function(k){ B[k] = k; }); return $assign({}, A)[S] != 7 || Object.keys($assign({}, B)).join('') != K; }) ? function assign(target, source){ // eslint-disable-line no-unused-vars var T = toObject(target) , aLen = arguments.length , index = 1 , getSymbols = gOPS.f , isEnum = pIE.f; while(aLen > index){ var S = IObject(arguments[index++]) , keys = getSymbols ? getKeys(S).concat(getSymbols(S)) : getKeys(S) , length = keys.length , j = 0 , key; while(length > j)if(isEnum.call(S, key = keys[j++]))T[key] = S[key]; } return T; } : $assign; /***/ }), /* 77 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.3.10 Object.is(value1, value2) var $export = __webpack_require__(15); $export($export.S, 'Object', {is: __webpack_require__(78)}); /***/ }), /* 78 */ /***/ (function(module, exports) { // 7.2.9 SameValue(x, y) module.exports = Object.is || function is(x, y){ return x === y ? x !== 0 || 1 / x === 1 / y : x != x && y != y; }; /***/ }), /* 79 */ /***/ (function(module, exports, __webpack_require__) { // 19.1.3.19 Object.setPrototypeOf(O, proto) var $export = __webpack_require__(15); $export($export.S, 'Object', {setPrototypeOf: __webpack_require__(80).set}); /***/ }), /* 80 */ /***/ (function(module, exports, __webpack_require__) { // Works with __proto__ only. Old v8 can't work with null proto objects. /* eslint-disable no-proto */ var isObject = __webpack_require__(20) , anObject = __webpack_require__(19); var check = function(O, proto){ anObject(O); if(!isObject(proto) && proto !== null)throw TypeError(proto + ": can't set as prototype!"); }; module.exports = { set: Object.setPrototypeOf || ('__proto__' in {} ? // eslint-disable-line function(test, buggy, set){ try { set = __webpack_require__(27)(Function.call, __webpack_require__(58).f(Object.prototype, '__proto__').set, 2); set(test, []); buggy = !(test instanceof Array); } catch(e){ buggy = true; } return function setPrototypeOf(O, proto){ check(O, proto); if(buggy)O.__proto__ = proto; else set(O, proto); return O; }; }({}, false) : undefined), check: check }; /***/ }), /* 81 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 19.1.3.6 Object.prototype.toString() var classof = __webpack_require__(82) , test = {}; test[__webpack_require__(32)('toStringTag')] = 'z'; if(test + '' != '[object z]'){ __webpack_require__(25)(Object.prototype, 'toString', function toString(){ return '[object ' + classof(this) + ']'; }, true); } /***/ }), /* 82 */ /***/ (function(module, exports, __webpack_require__) { // getting tag from 19.1.3.6 Object.prototype.toString() var cof = __webpack_require__(41) , TAG = __webpack_require__(32)('toStringTag') // ES3 wrong here , ARG = cof(function(){ return arguments; }()) == 'Arguments'; // fallback for IE11 Script Access Denied error var tryGet = function(it, key){ try { return it[key]; } catch(e){ /* empty */ } }; module.exports = function(it){ var O, T, B; return it === undefined ? 'Undefined' : it === null ? 'Null' // @@toStringTag case : typeof (T = tryGet(O = Object(it), TAG)) == 'string' ? T // builtinTag case : ARG ? cof(O) // ES3 arguments fallback : (B = cof(O)) == 'Object' && typeof O.callee == 'function' ? 'Arguments' : B; }; /***/ }), /* 83 */ /***/ (function(module, exports, __webpack_require__) { // 19.2.3.2 / 15.3.4.5 Function.prototype.bind(thisArg, args...) var $export = __webpack_require__(15); $export($export.P, 'Function', {bind: __webpack_require__(84)}); /***/ }), /* 84 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var aFunction = __webpack_require__(28) , isObject = __webpack_require__(20) , invoke = __webpack_require__(85) , arraySlice = [].slice , factories = {}; var construct = function(F, len, args){ if(!(len in factories)){ for(var n = [], i = 0; i < len; i++)n[i] = 'a[' + i + ']'; factories[len] = Function('F,a', 'return new F(' + n.join(',') + ')'); } return factories[len](F, args); }; module.exports = Function.bind || function bind(that /*, args... */){ var fn = aFunction(this) , partArgs = arraySlice.call(arguments, 1); var bound = function(/* args... */){ var args = partArgs.concat(arraySlice.call(arguments)); return this instanceof bound ? construct(fn, args.length, args) : invoke(fn, args, that); }; if(isObject(fn.prototype))bound.prototype = fn.prototype; return bound; }; /***/ }), /* 85 */ /***/ (function(module, exports) { // fast apply, http://jsperf.lnkit.com/fast-apply/5 module.exports = function(fn, args, that){ var un = that === undefined; switch(args.length){ case 0: return un ? fn() : fn.call(that); case 1: return un ? fn(args[0]) : fn.call(that, args[0]); case 2: return un ? fn(args[0], args[1]) : fn.call(that, args[0], args[1]); case 3: return un ? fn(args[0], args[1], args[2]) : fn.call(that, args[0], args[1], args[2]); case 4: return un ? fn(args[0], args[1], args[2], args[3]) : fn.call(that, args[0], args[1], args[2], args[3]); } return fn.apply(that, args); }; /***/ }), /* 86 */ /***/ (function(module, exports, __webpack_require__) { var dP = __webpack_require__(18).f , createDesc = __webpack_require__(24) , has = __webpack_require__(12) , FProto = Function.prototype , nameRE = /^\s*function ([^ (]*)/ , NAME = 'name'; var isExtensible = Object.isExtensible || function(){ return true; }; // 19.2.4.2 name NAME in FProto || __webpack_require__(13) && dP(FProto, NAME, { configurable: true, get: function(){ try { var that = this , name = ('' + that).match(nameRE)[1]; has(that, NAME) || !isExtensible(that) || dP(that, NAME, createDesc(5, name)); return name; } catch(e){ return ''; } } }); /***/ }), /* 87 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var isObject = __webpack_require__(20) , getPrototypeOf = __webpack_require__(66) , HAS_INSTANCE = __webpack_require__(32)('hasInstance') , FunctionProto = Function.prototype; // 19.2.3.6 Function.prototype[@@hasInstance](V) if(!(HAS_INSTANCE in FunctionProto))__webpack_require__(18).f(FunctionProto, HAS_INSTANCE, {value: function(O){ if(typeof this != 'function' || !isObject(O))return false; if(!isObject(this.prototype))return O instanceof this; // for environment w/o native `@@hasInstance` logic enough `instanceof`, but add this: while(O = getPrototypeOf(O))if(this.prototype === O)return true; return false; }}); /***/ }), /* 88 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , $parseInt = __webpack_require__(89); // 18.2.5 parseInt(string, radix) $export($export.G + $export.F * (parseInt != $parseInt), {parseInt: $parseInt}); /***/ }), /* 89 */ /***/ (function(module, exports, __webpack_require__) { var $parseInt = __webpack_require__(11).parseInt , $trim = __webpack_require__(90).trim , ws = __webpack_require__(91) , hex = /^[\-+]?0[xX]/; module.exports = $parseInt(ws + '08') !== 8 || $parseInt(ws + '0x16') !== 22 ? function parseInt(str, radix){ var string = $trim(String(str), 3); return $parseInt(string, (radix >>> 0) || (hex.test(string) ? 16 : 10)); } : $parseInt; /***/ }), /* 90 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , defined = __webpack_require__(42) , fails = __webpack_require__(14) , spaces = __webpack_require__(91) , space = '[' + spaces + ']' , non = '\u200b\u0085' , ltrim = RegExp('^' + space + space + '*') , rtrim = RegExp(space + space + '*$'); var exporter = function(KEY, exec, ALIAS){ var exp = {}; var FORCE = fails(function(){ return !!spaces[KEY]() || non[KEY]() != non; }); var fn = exp[KEY] = FORCE ? exec(trim) : spaces[KEY]; if(ALIAS)exp[ALIAS] = fn; $export($export.P + $export.F * FORCE, 'String', exp); }; // 1 -> String#trimLeft // 2 -> String#trimRight // 3 -> String#trim var trim = exporter.trim = function(string, TYPE){ string = String(defined(string)); if(TYPE & 1)string = string.replace(ltrim, ''); if(TYPE & 2)string = string.replace(rtrim, ''); return string; }; module.exports = exporter; /***/ }), /* 91 */ /***/ (function(module, exports) { module.exports = '\x09\x0A\x0B\x0C\x0D\x20\xA0\u1680\u180E\u2000\u2001\u2002\u2003' + '\u2004\u2005\u2006\u2007\u2008\u2009\u200A\u202F\u205F\u3000\u2028\u2029\uFEFF'; /***/ }), /* 92 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , $parseFloat = __webpack_require__(93); // 18.2.4 parseFloat(string) $export($export.G + $export.F * (parseFloat != $parseFloat), {parseFloat: $parseFloat}); /***/ }), /* 93 */ /***/ (function(module, exports, __webpack_require__) { var $parseFloat = __webpack_require__(11).parseFloat , $trim = __webpack_require__(90).trim; module.exports = 1 / $parseFloat(__webpack_require__(91) + '-0') !== -Infinity ? function parseFloat(str){ var string = $trim(String(str), 3) , result = $parseFloat(string); return result === 0 && string.charAt(0) == '-' ? -0 : result; } : $parseFloat; /***/ }), /* 94 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var global = __webpack_require__(11) , has = __webpack_require__(12) , cof = __webpack_require__(41) , inheritIfRequired = __webpack_require__(95) , toPrimitive = __webpack_require__(23) , fails = __webpack_require__(14) , gOPN = __webpack_require__(57).f , gOPD = __webpack_require__(58).f , dP = __webpack_require__(18).f , $trim = __webpack_require__(90).trim , NUMBER = 'Number' , $Number = global[NUMBER] , Base = $Number , proto = $Number.prototype // Opera ~12 has broken Object#toString , BROKEN_COF = cof(__webpack_require__(53)(proto)) == NUMBER , TRIM = 'trim' in String.prototype; // 7.1.3 ToNumber(argument) var toNumber = function(argument){ var it = toPrimitive(argument, false); if(typeof it == 'string' && it.length > 2){ it = TRIM ? it.trim() : $trim(it, 3); var first = it.charCodeAt(0) , third, radix, maxCode; if(first === 43 || first === 45){ third = it.charCodeAt(2); if(third === 88 || third === 120)return NaN; // Number('+0x1') should be NaN, old V8 fix } else if(first === 48){ switch(it.charCodeAt(1)){ case 66 : case 98 : radix = 2; maxCode = 49; break; // fast equal /^0b[01]+$/i case 79 : case 111 : radix = 8; maxCode = 55; break; // fast equal /^0o[0-7]+$/i default : return +it; } for(var digits = it.slice(2), i = 0, l = digits.length, code; i < l; i++){ code = digits.charCodeAt(i); // parseInt parses a string to a first unavailable symbol // but ToNumber should return NaN if a string contains unavailable symbols if(code < 48 || code > maxCode)return NaN; } return parseInt(digits, radix); } } return +it; }; if(!$Number(' 0o1') || !$Number('0b1') || $Number('+0x1')){ $Number = function Number(value){ var it = arguments.length < 1 ? 0 : value , that = this; return that instanceof $Number // check on 1..constructor(foo) case && (BROKEN_COF ? fails(function(){ proto.valueOf.call(that); }) : cof(that) != NUMBER) ? inheritIfRequired(new Base(toNumber(it)), that, $Number) : toNumber(it); }; for(var keys = __webpack_require__(13) ? gOPN(Base) : ( // ES3: 'MAX_VALUE,MIN_VALUE,NaN,NEGATIVE_INFINITY,POSITIVE_INFINITY,' + // ES6 (in case, if modules with ES6 Number statics required before): 'EPSILON,isFinite,isInteger,isNaN,isSafeInteger,MAX_SAFE_INTEGER,' + 'MIN_SAFE_INTEGER,parseFloat,parseInt,isInteger' ).split(','), j = 0, key; keys.length > j; j++){ if(has(Base, key = keys[j]) && !has($Number, key)){ dP($Number, key, gOPD(Base, key)); } } $Number.prototype = proto; proto.constructor = $Number; __webpack_require__(25)(global, NUMBER, $Number); } /***/ }), /* 95 */ /***/ (function(module, exports, __webpack_require__) { var isObject = __webpack_require__(20) , setPrototypeOf = __webpack_require__(80).set; module.exports = function(that, target, C){ var P, S = target.constructor; if(S !== C && typeof S == 'function' && (P = S.prototype) !== C.prototype && isObject(P) && setPrototypeOf){ setPrototypeOf(that, P); } return that; }; /***/ }), /* 96 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toInteger = __webpack_require__(45) , aNumberValue = __webpack_require__(97) , repeat = __webpack_require__(98) , $toFixed = 1..toFixed , floor = Math.floor , data = [0, 0, 0, 0, 0, 0] , ERROR = 'Number.toFixed: incorrect invocation!' , ZERO = '0'; var multiply = function(n, c){ var i = -1 , c2 = c; while(++i < 6){ c2 += n * data[i]; data[i] = c2 % 1e7; c2 = floor(c2 / 1e7); } }; var divide = function(n){ var i = 6 , c = 0; while(--i >= 0){ c += data[i]; data[i] = floor(c / n); c = (c % n) * 1e7; } }; var numToString = function(){ var i = 6 , s = ''; while(--i >= 0){ if(s !== '' || i === 0 || data[i] !== 0){ var t = String(data[i]); s = s === '' ? t : s + repeat.call(ZERO, 7 - t.length) + t; } } return s; }; var pow = function(x, n, acc){ return n === 0 ? acc : n % 2 === 1 ? pow(x, n - 1, acc * x) : pow(x * x, n / 2, acc); }; var log = function(x){ var n = 0 , x2 = x; while(x2 >= 4096){ n += 12; x2 /= 4096; } while(x2 >= 2){ n += 1; x2 /= 2; } return n; }; $export($export.P + $export.F * (!!$toFixed && ( 0.00008.toFixed(3) !== '0.000' || 0.9.toFixed(0) !== '1' || 1.255.toFixed(2) !== '1.25' || 1000000000000000128..toFixed(0) !== '1000000000000000128' ) || !__webpack_require__(14)(function(){ // V8 ~ Android 4.3- $toFixed.call({}); })), 'Number', { toFixed: function toFixed(fractionDigits){ var x = aNumberValue(this, ERROR) , f = toInteger(fractionDigits) , s = '' , m = ZERO , e, z, j, k; if(f < 0 || f > 20)throw RangeError(ERROR); if(x != x)return 'NaN'; if(x <= -1e21 || x >= 1e21)return String(x); if(x < 0){ s = '-'; x = -x; } if(x > 1e-21){ e = log(x * pow(2, 69, 1)) - 69; z = e < 0 ? x * pow(2, -e, 1) : x / pow(2, e, 1); z *= 0x10000000000000; e = 52 - e; if(e > 0){ multiply(0, z); j = f; while(j >= 7){ multiply(1e7, 0); j -= 7; } multiply(pow(10, j, 1), 0); j = e - 1; while(j >= 23){ divide(1 << 23); j -= 23; } divide(1 << j); multiply(1, 1); divide(2); m = numToString(); } else { multiply(0, z); multiply(1 << -e, 0); m = numToString() + repeat.call(ZERO, f); } } if(f > 0){ k = m.length; m = s + (k <= f ? '0.' + repeat.call(ZERO, f - k) + m : m.slice(0, k - f) + '.' + m.slice(k - f)); } else { m = s + m; } return m; } }); /***/ }), /* 97 */ /***/ (function(module, exports, __webpack_require__) { var cof = __webpack_require__(41); module.exports = function(it, msg){ if(typeof it != 'number' && cof(it) != 'Number')throw TypeError(msg); return +it; }; /***/ }), /* 98 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var toInteger = __webpack_require__(45) , defined = __webpack_require__(42); module.exports = function repeat(count){ var str = String(defined(this)) , res = '' , n = toInteger(count); if(n < 0 || n == Infinity)throw RangeError("Count can't be negative"); for(;n > 0; (n >>>= 1) && (str += str))if(n & 1)res += str; return res; }; /***/ }), /* 99 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $fails = __webpack_require__(14) , aNumberValue = __webpack_require__(97) , $toPrecision = 1..toPrecision; $export($export.P + $export.F * ($fails(function(){ // IE7- return $toPrecision.call(1, undefined) !== '1'; }) || !$fails(function(){ // V8 ~ Android 4.3- $toPrecision.call({}); })), 'Number', { toPrecision: function toPrecision(precision){ var that = aNumberValue(this, 'Number#toPrecision: incorrect invocation!'); return precision === undefined ? $toPrecision.call(that) : $toPrecision.call(that, precision); } }); /***/ }), /* 100 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.1 Number.EPSILON var $export = __webpack_require__(15); $export($export.S, 'Number', {EPSILON: Math.pow(2, -52)}); /***/ }), /* 101 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.2 Number.isFinite(number) var $export = __webpack_require__(15) , _isFinite = __webpack_require__(11).isFinite; $export($export.S, 'Number', { isFinite: function isFinite(it){ return typeof it == 'number' && _isFinite(it); } }); /***/ }), /* 102 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.3 Number.isInteger(number) var $export = __webpack_require__(15); $export($export.S, 'Number', {isInteger: __webpack_require__(103)}); /***/ }), /* 103 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.3 Number.isInteger(number) var isObject = __webpack_require__(20) , floor = Math.floor; module.exports = function isInteger(it){ return !isObject(it) && isFinite(it) && floor(it) === it; }; /***/ }), /* 104 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.4 Number.isNaN(number) var $export = __webpack_require__(15); $export($export.S, 'Number', { isNaN: function isNaN(number){ return number != number; } }); /***/ }), /* 105 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.5 Number.isSafeInteger(number) var $export = __webpack_require__(15) , isInteger = __webpack_require__(103) , abs = Math.abs; $export($export.S, 'Number', { isSafeInteger: function isSafeInteger(number){ return isInteger(number) && abs(number) <= 0x1fffffffffffff; } }); /***/ }), /* 106 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.6 Number.MAX_SAFE_INTEGER var $export = __webpack_require__(15); $export($export.S, 'Number', {MAX_SAFE_INTEGER: 0x1fffffffffffff}); /***/ }), /* 107 */ /***/ (function(module, exports, __webpack_require__) { // 20.1.2.10 Number.MIN_SAFE_INTEGER var $export = __webpack_require__(15); $export($export.S, 'Number', {MIN_SAFE_INTEGER: -0x1fffffffffffff}); /***/ }), /* 108 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , $parseFloat = __webpack_require__(93); // 20.1.2.12 Number.parseFloat(string) $export($export.S + $export.F * (Number.parseFloat != $parseFloat), 'Number', {parseFloat: $parseFloat}); /***/ }), /* 109 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , $parseInt = __webpack_require__(89); // 20.1.2.13 Number.parseInt(string, radix) $export($export.S + $export.F * (Number.parseInt != $parseInt), 'Number', {parseInt: $parseInt}); /***/ }), /* 110 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.3 Math.acosh(x) var $export = __webpack_require__(15) , log1p = __webpack_require__(111) , sqrt = Math.sqrt , $acosh = Math.acosh; $export($export.S + $export.F * !($acosh // V8 bug: https://code.google.com/p/v8/issues/detail?id=3509 && Math.floor($acosh(Number.MAX_VALUE)) == 710 // Tor Browser bug: Math.acosh(Infinity) -> NaN && $acosh(Infinity) == Infinity ), 'Math', { acosh: function acosh(x){ return (x = +x) < 1 ? NaN : x > 94906265.62425156 ? Math.log(x) + Math.LN2 : log1p(x - 1 + sqrt(x - 1) * sqrt(x + 1)); } }); /***/ }), /* 111 */ /***/ (function(module, exports) { // 20.2.2.20 Math.log1p(x) module.exports = Math.log1p || function log1p(x){ return (x = +x) > -1e-8 && x < 1e-8 ? x - x * x / 2 : Math.log(1 + x); }; /***/ }), /* 112 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.5 Math.asinh(x) var $export = __webpack_require__(15) , $asinh = Math.asinh; function asinh(x){ return !isFinite(x = +x) || x == 0 ? x : x < 0 ? -asinh(-x) : Math.log(x + Math.sqrt(x * x + 1)); } // Tor Browser bug: Math.asinh(0) -> -0 $export($export.S + $export.F * !($asinh && 1 / $asinh(0) > 0), 'Math', {asinh: asinh}); /***/ }), /* 113 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.7 Math.atanh(x) var $export = __webpack_require__(15) , $atanh = Math.atanh; // Tor Browser bug: Math.atanh(-0) -> 0 $export($export.S + $export.F * !($atanh && 1 / $atanh(-0) < 0), 'Math', { atanh: function atanh(x){ return (x = +x) == 0 ? x : Math.log((1 + x) / (1 - x)) / 2; } }); /***/ }), /* 114 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.9 Math.cbrt(x) var $export = __webpack_require__(15) , sign = __webpack_require__(115); $export($export.S, 'Math', { cbrt: function cbrt(x){ return sign(x = +x) * Math.pow(Math.abs(x), 1 / 3); } }); /***/ }), /* 115 */ /***/ (function(module, exports) { // 20.2.2.28 Math.sign(x) module.exports = Math.sign || function sign(x){ return (x = +x) == 0 || x != x ? x : x < 0 ? -1 : 1; }; /***/ }), /* 116 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.11 Math.clz32(x) var $export = __webpack_require__(15); $export($export.S, 'Math', { clz32: function clz32(x){ return (x >>>= 0) ? 31 - Math.floor(Math.log(x + 0.5) * Math.LOG2E) : 32; } }); /***/ }), /* 117 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.12 Math.cosh(x) var $export = __webpack_require__(15) , exp = Math.exp; $export($export.S, 'Math', { cosh: function cosh(x){ return (exp(x = +x) + exp(-x)) / 2; } }); /***/ }), /* 118 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.14 Math.expm1(x) var $export = __webpack_require__(15) , $expm1 = __webpack_require__(119); $export($export.S + $export.F * ($expm1 != Math.expm1), 'Math', {expm1: $expm1}); /***/ }), /* 119 */ /***/ (function(module, exports) { // 20.2.2.14 Math.expm1(x) var $expm1 = Math.expm1; module.exports = (!$expm1 // Old FF bug || $expm1(10) > 22025.465794806719 || $expm1(10) < 22025.4657948067165168 // Tor Browser bug || $expm1(-2e-17) != -2e-17 ) ? function expm1(x){ return (x = +x) == 0 ? x : x > -1e-6 && x < 1e-6 ? x + x * x / 2 : Math.exp(x) - 1; } : $expm1; /***/ }), /* 120 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.16 Math.fround(x) var $export = __webpack_require__(15) , sign = __webpack_require__(115) , pow = Math.pow , EPSILON = pow(2, -52) , EPSILON32 = pow(2, -23) , MAX32 = pow(2, 127) * (2 - EPSILON32) , MIN32 = pow(2, -126); var roundTiesToEven = function(n){ return n + 1 / EPSILON - 1 / EPSILON; }; $export($export.S, 'Math', { fround: function fround(x){ var $abs = Math.abs(x) , $sign = sign(x) , a, result; if($abs < MIN32)return $sign * roundTiesToEven($abs / MIN32 / EPSILON32) * MIN32 * EPSILON32; a = (1 + EPSILON32 / EPSILON) * $abs; result = a - (a - $abs); if(result > MAX32 || result != result)return $sign * Infinity; return $sign * result; } }); /***/ }), /* 121 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.17 Math.hypot([value1[, value2[, … ]]]) var $export = __webpack_require__(15) , abs = Math.abs; $export($export.S, 'Math', { hypot: function hypot(value1, value2){ // eslint-disable-line no-unused-vars var sum = 0 , i = 0 , aLen = arguments.length , larg = 0 , arg, div; while(i < aLen){ arg = abs(arguments[i++]); if(larg < arg){ div = larg / arg; sum = sum * div * div + 1; larg = arg; } else if(arg > 0){ div = arg / larg; sum += div * div; } else sum += arg; } return larg === Infinity ? Infinity : larg * Math.sqrt(sum); } }); /***/ }), /* 122 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.18 Math.imul(x, y) var $export = __webpack_require__(15) , $imul = Math.imul; // some WebKit versions fails with big numbers, some has wrong arity $export($export.S + $export.F * __webpack_require__(14)(function(){ return $imul(0xffffffff, 5) != -5 || $imul.length != 2; }), 'Math', { imul: function imul(x, y){ var UINT16 = 0xffff , xn = +x , yn = +y , xl = UINT16 & xn , yl = UINT16 & yn; return 0 | xl * yl + ((UINT16 & xn >>> 16) * yl + xl * (UINT16 & yn >>> 16) << 16 >>> 0); } }); /***/ }), /* 123 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.21 Math.log10(x) var $export = __webpack_require__(15); $export($export.S, 'Math', { log10: function log10(x){ return Math.log(x) / Math.LN10; } }); /***/ }), /* 124 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.20 Math.log1p(x) var $export = __webpack_require__(15); $export($export.S, 'Math', {log1p: __webpack_require__(111)}); /***/ }), /* 125 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.22 Math.log2(x) var $export = __webpack_require__(15); $export($export.S, 'Math', { log2: function log2(x){ return Math.log(x) / Math.LN2; } }); /***/ }), /* 126 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.28 Math.sign(x) var $export = __webpack_require__(15); $export($export.S, 'Math', {sign: __webpack_require__(115)}); /***/ }), /* 127 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.30 Math.sinh(x) var $export = __webpack_require__(15) , expm1 = __webpack_require__(119) , exp = Math.exp; // V8 near Chromium 38 has a problem with very small numbers $export($export.S + $export.F * __webpack_require__(14)(function(){ return !Math.sinh(-2e-17) != -2e-17; }), 'Math', { sinh: function sinh(x){ return Math.abs(x = +x) < 1 ? (expm1(x) - expm1(-x)) / 2 : (exp(x - 1) - exp(-x - 1)) * (Math.E / 2); } }); /***/ }), /* 128 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.33 Math.tanh(x) var $export = __webpack_require__(15) , expm1 = __webpack_require__(119) , exp = Math.exp; $export($export.S, 'Math', { tanh: function tanh(x){ var a = expm1(x = +x) , b = expm1(-x); return a == Infinity ? 1 : b == Infinity ? -1 : (a - b) / (exp(x) + exp(-x)); } }); /***/ }), /* 129 */ /***/ (function(module, exports, __webpack_require__) { // 20.2.2.34 Math.trunc(x) var $export = __webpack_require__(15); $export($export.S, 'Math', { trunc: function trunc(it){ return (it > 0 ? Math.floor : Math.ceil)(it); } }); /***/ }), /* 130 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , toIndex = __webpack_require__(46) , fromCharCode = String.fromCharCode , $fromCodePoint = String.fromCodePoint; // length should be 1, old FF problem $export($export.S + $export.F * (!!$fromCodePoint && $fromCodePoint.length != 1), 'String', { // 21.1.2.2 String.fromCodePoint(...codePoints) fromCodePoint: function fromCodePoint(x){ // eslint-disable-line no-unused-vars var res = [] , aLen = arguments.length , i = 0 , code; while(aLen > i){ code = +arguments[i++]; if(toIndex(code, 0x10ffff) !== code)throw RangeError(code + ' is not a valid code point'); res.push(code < 0x10000 ? fromCharCode(code) : fromCharCode(((code -= 0x10000) >> 10) + 0xd800, code % 0x400 + 0xdc00) ); } return res.join(''); } }); /***/ }), /* 131 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , toIObject = __webpack_require__(39) , toLength = __webpack_require__(44); $export($export.S, 'String', { // 21.1.2.4 String.raw(callSite, ...substitutions) raw: function raw(callSite){ var tpl = toIObject(callSite.raw) , len = toLength(tpl.length) , aLen = arguments.length , res = [] , i = 0; while(len > i){ res.push(String(tpl[i++])); if(i < aLen)res.push(String(arguments[i])); } return res.join(''); } }); /***/ }), /* 132 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 21.1.3.25 String.prototype.trim() __webpack_require__(90)('trim', function($trim){ return function trim(){ return $trim(this, 3); }; }); /***/ }), /* 133 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $at = __webpack_require__(134)(true); // 21.1.3.27 String.prototype[@@iterator]() __webpack_require__(135)(String, 'String', function(iterated){ this._t = String(iterated); // target this._i = 0; // next index // 21.1.5.2.1 %StringIteratorPrototype%.next() }, function(){ var O = this._t , index = this._i , point; if(index >= O.length)return {value: undefined, done: true}; point = $at(O, index); this._i += point.length; return {value: point, done: false}; }); /***/ }), /* 134 */ /***/ (function(module, exports, __webpack_require__) { var toInteger = __webpack_require__(45) , defined = __webpack_require__(42); // true -> String#at // false -> String#codePointAt module.exports = function(TO_STRING){ return function(that, pos){ var s = String(defined(that)) , i = toInteger(pos) , l = s.length , a, b; if(i < 0 || i >= l)return TO_STRING ? '' : undefined; a = s.charCodeAt(i); return a < 0xd800 || a > 0xdbff || i + 1 === l || (b = s.charCodeAt(i + 1)) < 0xdc00 || b > 0xdfff ? TO_STRING ? s.charAt(i) : a : TO_STRING ? s.slice(i, i + 2) : (a - 0xd800 << 10) + (b - 0xdc00) + 0x10000; }; }; /***/ }), /* 135 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var LIBRARY = __webpack_require__(35) , $export = __webpack_require__(15) , redefine = __webpack_require__(25) , hide = __webpack_require__(17) , has = __webpack_require__(12) , Iterators = __webpack_require__(136) , $iterCreate = __webpack_require__(137) , setToStringTag = __webpack_require__(31) , getPrototypeOf = __webpack_require__(66) , ITERATOR = __webpack_require__(32)('iterator') , BUGGY = !([].keys && 'next' in [].keys()) // Safari has buggy iterators w/o `next` , FF_ITERATOR = '@@iterator' , KEYS = 'keys' , VALUES = 'values'; var returnThis = function(){ return this; }; module.exports = function(Base, NAME, Constructor, next, DEFAULT, IS_SET, FORCED){ $iterCreate(Constructor, NAME, next); var getMethod = function(kind){ if(!BUGGY && kind in proto)return proto[kind]; switch(kind){ case KEYS: return function keys(){ return new Constructor(this, kind); }; case VALUES: return function values(){ return new Constructor(this, kind); }; } return function entries(){ return new Constructor(this, kind); }; }; var TAG = NAME + ' Iterator' , DEF_VALUES = DEFAULT == VALUES , VALUES_BUG = false , proto = Base.prototype , $native = proto[ITERATOR] || proto[FF_ITERATOR] || DEFAULT && proto[DEFAULT] , $default = $native || getMethod(DEFAULT) , $entries = DEFAULT ? !DEF_VALUES ? $default : getMethod('entries') : undefined , $anyNative = NAME == 'Array' ? proto.entries || $native : $native , methods, key, IteratorPrototype; // Fix native if($anyNative){ IteratorPrototype = getPrototypeOf($anyNative.call(new Base)); if(IteratorPrototype !== Object.prototype){ // Set @@toStringTag to native iterators setToStringTag(IteratorPrototype, TAG, true); // fix for some old engines if(!LIBRARY && !has(IteratorPrototype, ITERATOR))hide(IteratorPrototype, ITERATOR, returnThis); } } // fix Array#{values, @@iterator}.name in V8 / FF if(DEF_VALUES && $native && $native.name !== VALUES){ VALUES_BUG = true; $default = function values(){ return $native.call(this); }; } // Define iterator if((!LIBRARY || FORCED) && (BUGGY || VALUES_BUG || !proto[ITERATOR])){ hide(proto, ITERATOR, $default); } // Plug for library Iterators[NAME] = $default; Iterators[TAG] = returnThis; if(DEFAULT){ methods = { values: DEF_VALUES ? $default : getMethod(VALUES), keys: IS_SET ? $default : getMethod(KEYS), entries: $entries }; if(FORCED)for(key in methods){ if(!(key in proto))redefine(proto, key, methods[key]); } else $export($export.P + $export.F * (BUGGY || VALUES_BUG), NAME, methods); } return methods; }; /***/ }), /* 136 */ /***/ (function(module, exports) { module.exports = {}; /***/ }), /* 137 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var create = __webpack_require__(53) , descriptor = __webpack_require__(24) , setToStringTag = __webpack_require__(31) , IteratorPrototype = {}; // 25.1.2.1.1 %IteratorPrototype%[@@iterator]() __webpack_require__(17)(IteratorPrototype, __webpack_require__(32)('iterator'), function(){ return this; }); module.exports = function(Constructor, NAME, next){ Constructor.prototype = create(IteratorPrototype, {next: descriptor(1, next)}); setToStringTag(Constructor, NAME + ' Iterator'); }; /***/ }), /* 138 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $at = __webpack_require__(134)(false); $export($export.P, 'String', { // 21.1.3.3 String.prototype.codePointAt(pos) codePointAt: function codePointAt(pos){ return $at(this, pos); } }); /***/ }), /* 139 */ /***/ (function(module, exports, __webpack_require__) { // 21.1.3.6 String.prototype.endsWith(searchString [, endPosition]) 'use strict'; var $export = __webpack_require__(15) , toLength = __webpack_require__(44) , context = __webpack_require__(140) , ENDS_WITH = 'endsWith' , $endsWith = ''[ENDS_WITH]; $export($export.P + $export.F * __webpack_require__(142)(ENDS_WITH), 'String', { endsWith: function endsWith(searchString /*, endPosition = @length */){ var that = context(this, searchString, ENDS_WITH) , endPosition = arguments.length > 1 ? arguments[1] : undefined , len = toLength(that.length) , end = endPosition === undefined ? len : Math.min(toLength(endPosition), len) , search = String(searchString); return $endsWith ? $endsWith.call(that, search, end) : that.slice(end - search.length, end) === search; } }); /***/ }), /* 140 */ /***/ (function(module, exports, __webpack_require__) { // helper for String#{startsWith, endsWith, includes} var isRegExp = __webpack_require__(141) , defined = __webpack_require__(42); module.exports = function(that, searchString, NAME){ if(isRegExp(searchString))throw TypeError('String#' + NAME + " doesn't accept regex!"); return String(defined(that)); }; /***/ }), /* 141 */ /***/ (function(module, exports, __webpack_require__) { // 7.2.8 IsRegExp(argument) var isObject = __webpack_require__(20) , cof = __webpack_require__(41) , MATCH = __webpack_require__(32)('match'); module.exports = function(it){ var isRegExp; return isObject(it) && ((isRegExp = it[MATCH]) !== undefined ? !!isRegExp : cof(it) == 'RegExp'); }; /***/ }), /* 142 */ /***/ (function(module, exports, __webpack_require__) { var MATCH = __webpack_require__(32)('match'); module.exports = function(KEY){ var re = /./; try { '/./'[KEY](re); } catch(e){ try { re[MATCH] = false; return !'/./'[KEY](re); } catch(f){ /* empty */ } } return true; }; /***/ }), /* 143 */ /***/ (function(module, exports, __webpack_require__) { // 21.1.3.7 String.prototype.includes(searchString, position = 0) 'use strict'; var $export = __webpack_require__(15) , context = __webpack_require__(140) , INCLUDES = 'includes'; $export($export.P + $export.F * __webpack_require__(142)(INCLUDES), 'String', { includes: function includes(searchString /*, position = 0 */){ return !!~context(this, searchString, INCLUDES) .indexOf(searchString, arguments.length > 1 ? arguments[1] : undefined); } }); /***/ }), /* 144 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15); $export($export.P, 'String', { // 21.1.3.13 String.prototype.repeat(count) repeat: __webpack_require__(98) }); /***/ }), /* 145 */ /***/ (function(module, exports, __webpack_require__) { // 21.1.3.18 String.prototype.startsWith(searchString [, position ]) 'use strict'; var $export = __webpack_require__(15) , toLength = __webpack_require__(44) , context = __webpack_require__(140) , STARTS_WITH = 'startsWith' , $startsWith = ''[STARTS_WITH]; $export($export.P + $export.F * __webpack_require__(142)(STARTS_WITH), 'String', { startsWith: function startsWith(searchString /*, position = 0 */){ var that = context(this, searchString, STARTS_WITH) , index = toLength(Math.min(arguments.length > 1 ? arguments[1] : undefined, that.length)) , search = String(searchString); return $startsWith ? $startsWith.call(that, search, index) : that.slice(index, index + search.length) === search; } }); /***/ }), /* 146 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.2 String.prototype.anchor(name) __webpack_require__(147)('anchor', function(createHTML){ return function anchor(name){ return createHTML(this, 'a', 'name', name); } }); /***/ }), /* 147 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , fails = __webpack_require__(14) , defined = __webpack_require__(42) , quot = /"/g; // B.2.3.2.1 CreateHTML(string, tag, attribute, value) var createHTML = function(string, tag, attribute, value) { var S = String(defined(string)) , p1 = '<' + tag; if(attribute !== '')p1 += ' ' + attribute + '="' + String(value).replace(quot, '"') + '"'; return p1 + '>' + S + '</' + tag + '>'; }; module.exports = function(NAME, exec){ var O = {}; O[NAME] = exec(createHTML); $export($export.P + $export.F * fails(function(){ var test = ''[NAME]('"'); return test !== test.toLowerCase() || test.split('"').length > 3; }), 'String', O); }; /***/ }), /* 148 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.3 String.prototype.big() __webpack_require__(147)('big', function(createHTML){ return function big(){ return createHTML(this, 'big', '', ''); } }); /***/ }), /* 149 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.4 String.prototype.blink() __webpack_require__(147)('blink', function(createHTML){ return function blink(){ return createHTML(this, 'blink', '', ''); } }); /***/ }), /* 150 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.5 String.prototype.bold() __webpack_require__(147)('bold', function(createHTML){ return function bold(){ return createHTML(this, 'b', '', ''); } }); /***/ }), /* 151 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.6 String.prototype.fixed() __webpack_require__(147)('fixed', function(createHTML){ return function fixed(){ return createHTML(this, 'tt', '', ''); } }); /***/ }), /* 152 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.7 String.prototype.fontcolor(color) __webpack_require__(147)('fontcolor', function(createHTML){ return function fontcolor(color){ return createHTML(this, 'font', 'color', color); } }); /***/ }), /* 153 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.8 String.prototype.fontsize(size) __webpack_require__(147)('fontsize', function(createHTML){ return function fontsize(size){ return createHTML(this, 'font', 'size', size); } }); /***/ }), /* 154 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.9 String.prototype.italics() __webpack_require__(147)('italics', function(createHTML){ return function italics(){ return createHTML(this, 'i', '', ''); } }); /***/ }), /* 155 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.10 String.prototype.link(url) __webpack_require__(147)('link', function(createHTML){ return function link(url){ return createHTML(this, 'a', 'href', url); } }); /***/ }), /* 156 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.11 String.prototype.small() __webpack_require__(147)('small', function(createHTML){ return function small(){ return createHTML(this, 'small', '', ''); } }); /***/ }), /* 157 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.12 String.prototype.strike() __webpack_require__(147)('strike', function(createHTML){ return function strike(){ return createHTML(this, 'strike', '', ''); } }); /***/ }), /* 158 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.13 String.prototype.sub() __webpack_require__(147)('sub', function(createHTML){ return function sub(){ return createHTML(this, 'sub', '', ''); } }); /***/ }), /* 159 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // B.2.3.14 String.prototype.sup() __webpack_require__(147)('sup', function(createHTML){ return function sup(){ return createHTML(this, 'sup', '', ''); } }); /***/ }), /* 160 */ /***/ (function(module, exports, __webpack_require__) { // 20.3.3.1 / 15.9.4.4 Date.now() var $export = __webpack_require__(15); $export($export.S, 'Date', {now: function(){ return new Date().getTime(); }}); /***/ }), /* 161 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toObject = __webpack_require__(65) , toPrimitive = __webpack_require__(23); $export($export.P + $export.F * __webpack_require__(14)(function(){ return new Date(NaN).toJSON() !== null || Date.prototype.toJSON.call({toISOString: function(){ return 1; }}) !== 1; }), 'Date', { toJSON: function toJSON(key){ var O = toObject(this) , pv = toPrimitive(O); return typeof pv == 'number' && !isFinite(pv) ? null : O.toISOString(); } }); /***/ }), /* 162 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 20.3.4.36 / 15.9.5.43 Date.prototype.toISOString() var $export = __webpack_require__(15) , fails = __webpack_require__(14) , getTime = Date.prototype.getTime; var lz = function(num){ return num > 9 ? num : '0' + num; }; // PhantomJS / old WebKit has a broken implementations $export($export.P + $export.F * (fails(function(){ return new Date(-5e13 - 1).toISOString() != '0385-07-25T07:06:39.999Z'; }) || !fails(function(){ new Date(NaN).toISOString(); })), 'Date', { toISOString: function toISOString(){ if(!isFinite(getTime.call(this)))throw RangeError('Invalid time value'); var d = this , y = d.getUTCFullYear() , m = d.getUTCMilliseconds() , s = y < 0 ? '-' : y > 9999 ? '+' : ''; return s + ('00000' + Math.abs(y)).slice(s ? -6 : -4) + '-' + lz(d.getUTCMonth() + 1) + '-' + lz(d.getUTCDate()) + 'T' + lz(d.getUTCHours()) + ':' + lz(d.getUTCMinutes()) + ':' + lz(d.getUTCSeconds()) + '.' + (m > 99 ? m : '0' + lz(m)) + 'Z'; } }); /***/ }), /* 163 */ /***/ (function(module, exports, __webpack_require__) { var DateProto = Date.prototype , INVALID_DATE = 'Invalid Date' , TO_STRING = 'toString' , $toString = DateProto[TO_STRING] , getTime = DateProto.getTime; if(new Date(NaN) + '' != INVALID_DATE){ __webpack_require__(25)(DateProto, TO_STRING, function toString(){ var value = getTime.call(this); return value === value ? $toString.call(this) : INVALID_DATE; }); } /***/ }), /* 164 */ /***/ (function(module, exports, __webpack_require__) { var TO_PRIMITIVE = __webpack_require__(32)('toPrimitive') , proto = Date.prototype; if(!(TO_PRIMITIVE in proto))__webpack_require__(17)(proto, TO_PRIMITIVE, __webpack_require__(165)); /***/ }), /* 165 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var anObject = __webpack_require__(19) , toPrimitive = __webpack_require__(23) , NUMBER = 'number'; module.exports = function(hint){ if(hint !== 'string' && hint !== NUMBER && hint !== 'default')throw TypeError('Incorrect hint'); return toPrimitive(anObject(this), hint != NUMBER); }; /***/ }), /* 166 */ /***/ (function(module, exports, __webpack_require__) { // 22.1.2.2 / 15.4.3.2 Array.isArray(arg) var $export = __webpack_require__(15); $export($export.S, 'Array', {isArray: __webpack_require__(52)}); /***/ }), /* 167 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var ctx = __webpack_require__(27) , $export = __webpack_require__(15) , toObject = __webpack_require__(65) , call = __webpack_require__(168) , isArrayIter = __webpack_require__(169) , toLength = __webpack_require__(44) , createProperty = __webpack_require__(170) , getIterFn = __webpack_require__(171); $export($export.S + $export.F * !__webpack_require__(172)(function(iter){ Array.from(iter); }), 'Array', { // 22.1.2.1 Array.from(arrayLike, mapfn = undefined, thisArg = undefined) from: function from(arrayLike/*, mapfn = undefined, thisArg = undefined*/){ var O = toObject(arrayLike) , C = typeof this == 'function' ? this : Array , aLen = arguments.length , mapfn = aLen > 1 ? arguments[1] : undefined , mapping = mapfn !== undefined , index = 0 , iterFn = getIterFn(O) , length, result, step, iterator; if(mapping)mapfn = ctx(mapfn, aLen > 2 ? arguments[2] : undefined, 2); // if object isn't iterable or it's array with default iterator - use simple case if(iterFn != undefined && !(C == Array && isArrayIter(iterFn))){ for(iterator = iterFn.call(O), result = new C; !(step = iterator.next()).done; index++){ createProperty(result, index, mapping ? call(iterator, mapfn, [step.value, index], true) : step.value); } } else { length = toLength(O.length); for(result = new C(length); length > index; index++){ createProperty(result, index, mapping ? mapfn(O[index], index) : O[index]); } } result.length = index; return result; } }); /***/ }), /* 168 */ /***/ (function(module, exports, __webpack_require__) { // call something on iterator step with safe closing on error var anObject = __webpack_require__(19); module.exports = function(iterator, fn, value, entries){ try { return entries ? fn(anObject(value)[0], value[1]) : fn(value); // 7.4.6 IteratorClose(iterator, completion) } catch(e){ var ret = iterator['return']; if(ret !== undefined)anObject(ret.call(iterator)); throw e; } }; /***/ }), /* 169 */ /***/ (function(module, exports, __webpack_require__) { // check on default Array iterator var Iterators = __webpack_require__(136) , ITERATOR = __webpack_require__(32)('iterator') , ArrayProto = Array.prototype; module.exports = function(it){ return it !== undefined && (Iterators.Array === it || ArrayProto[ITERATOR] === it); }; /***/ }), /* 170 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $defineProperty = __webpack_require__(18) , createDesc = __webpack_require__(24); module.exports = function(object, index, value){ if(index in object)$defineProperty.f(object, index, createDesc(0, value)); else object[index] = value; }; /***/ }), /* 171 */ /***/ (function(module, exports, __webpack_require__) { var classof = __webpack_require__(82) , ITERATOR = __webpack_require__(32)('iterator') , Iterators = __webpack_require__(136); module.exports = __webpack_require__(16).getIteratorMethod = function(it){ if(it != undefined)return it[ITERATOR] || it['@@iterator'] || Iterators[classof(it)]; }; /***/ }), /* 172 */ /***/ (function(module, exports, __webpack_require__) { var ITERATOR = __webpack_require__(32)('iterator') , SAFE_CLOSING = false; try { var riter = [7][ITERATOR](); riter['return'] = function(){ SAFE_CLOSING = true; }; Array.from(riter, function(){ throw 2; }); } catch(e){ /* empty */ } module.exports = function(exec, skipClosing){ if(!skipClosing && !SAFE_CLOSING)return false; var safe = false; try { var arr = [7] , iter = arr[ITERATOR](); iter.next = function(){ return {done: safe = true}; }; arr[ITERATOR] = function(){ return iter; }; exec(arr); } catch(e){ /* empty */ } return safe; }; /***/ }), /* 173 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , createProperty = __webpack_require__(170); // WebKit Array.of isn't generic $export($export.S + $export.F * __webpack_require__(14)(function(){ function F(){} return !(Array.of.call(F) instanceof F); }), 'Array', { // 22.1.2.3 Array.of( ...items) of: function of(/* ...args */){ var index = 0 , aLen = arguments.length , result = new (typeof this == 'function' ? this : Array)(aLen); while(aLen > index)createProperty(result, index, arguments[index++]); result.length = aLen; return result; } }); /***/ }), /* 174 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 22.1.3.13 Array.prototype.join(separator) var $export = __webpack_require__(15) , toIObject = __webpack_require__(39) , arrayJoin = [].join; // fallback for not array-like strings $export($export.P + $export.F * (__webpack_require__(40) != Object || !__webpack_require__(175)(arrayJoin)), 'Array', { join: function join(separator){ return arrayJoin.call(toIObject(this), separator === undefined ? ',' : separator); } }); /***/ }), /* 175 */ /***/ (function(module, exports, __webpack_require__) { var fails = __webpack_require__(14); module.exports = function(method, arg){ return !!method && fails(function(){ arg ? method.call(null, function(){}, 1) : method.call(null); }); }; /***/ }), /* 176 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , html = __webpack_require__(55) , cof = __webpack_require__(41) , toIndex = __webpack_require__(46) , toLength = __webpack_require__(44) , arraySlice = [].slice; // fallback for not array-like ES3 strings and DOM objects $export($export.P + $export.F * __webpack_require__(14)(function(){ if(html)arraySlice.call(html); }), 'Array', { slice: function slice(begin, end){ var len = toLength(this.length) , klass = cof(this); end = end === undefined ? len : end; if(klass == 'Array')return arraySlice.call(this, begin, end); var start = toIndex(begin, len) , upTo = toIndex(end, len) , size = toLength(upTo - start) , cloned = Array(size) , i = 0; for(; i < size; i++)cloned[i] = klass == 'String' ? this.charAt(start + i) : this[start + i]; return cloned; } }); /***/ }), /* 177 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , aFunction = __webpack_require__(28) , toObject = __webpack_require__(65) , fails = __webpack_require__(14) , $sort = [].sort , test = [1, 2, 3]; $export($export.P + $export.F * (fails(function(){ // IE8- test.sort(undefined); }) || !fails(function(){ // V8 bug test.sort(null); // Old WebKit }) || !__webpack_require__(175)($sort)), 'Array', { // 22.1.3.25 Array.prototype.sort(comparefn) sort: function sort(comparefn){ return comparefn === undefined ? $sort.call(toObject(this)) : $sort.call(toObject(this), aFunction(comparefn)); } }); /***/ }), /* 178 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $forEach = __webpack_require__(179)(0) , STRICT = __webpack_require__(175)([].forEach, true); $export($export.P + $export.F * !STRICT, 'Array', { // 22.1.3.10 / 15.4.4.18 Array.prototype.forEach(callbackfn [, thisArg]) forEach: function forEach(callbackfn /* , thisArg */){ return $forEach(this, callbackfn, arguments[1]); } }); /***/ }), /* 179 */ /***/ (function(module, exports, __webpack_require__) { // 0 -> Array#forEach // 1 -> Array#map // 2 -> Array#filter // 3 -> Array#some // 4 -> Array#every // 5 -> Array#find // 6 -> Array#findIndex var ctx = __webpack_require__(27) , IObject = __webpack_require__(40) , toObject = __webpack_require__(65) , toLength = __webpack_require__(44) , asc = __webpack_require__(180); module.exports = function(TYPE, $create){ var IS_MAP = TYPE == 1 , IS_FILTER = TYPE == 2 , IS_SOME = TYPE == 3 , IS_EVERY = TYPE == 4 , IS_FIND_INDEX = TYPE == 6 , NO_HOLES = TYPE == 5 || IS_FIND_INDEX , create = $create || asc; return function($this, callbackfn, that){ var O = toObject($this) , self = IObject(O) , f = ctx(callbackfn, that, 3) , length = toLength(self.length) , index = 0 , result = IS_MAP ? create($this, length) : IS_FILTER ? create($this, 0) : undefined , val, res; for(;length > index; index++)if(NO_HOLES || index in self){ val = self[index]; res = f(val, index, O); if(TYPE){ if(IS_MAP)result[index] = res; // map else if(res)switch(TYPE){ case 3: return true; // some case 5: return val; // find case 6: return index; // findIndex case 2: result.push(val); // filter } else if(IS_EVERY)return false; // every } } return IS_FIND_INDEX ? -1 : IS_SOME || IS_EVERY ? IS_EVERY : result; }; }; /***/ }), /* 180 */ /***/ (function(module, exports, __webpack_require__) { // 9.4.2.3 ArraySpeciesCreate(originalArray, length) var speciesConstructor = __webpack_require__(181); module.exports = function(original, length){ return new (speciesConstructor(original))(length); }; /***/ }), /* 181 */ /***/ (function(module, exports, __webpack_require__) { var isObject = __webpack_require__(20) , isArray = __webpack_require__(52) , SPECIES = __webpack_require__(32)('species'); module.exports = function(original){ var C; if(isArray(original)){ C = original.constructor; // cross-realm fallback if(typeof C == 'function' && (C === Array || isArray(C.prototype)))C = undefined; if(isObject(C)){ C = C[SPECIES]; if(C === null)C = undefined; } } return C === undefined ? Array : C; }; /***/ }), /* 182 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $map = __webpack_require__(179)(1); $export($export.P + $export.F * !__webpack_require__(175)([].map, true), 'Array', { // 22.1.3.15 / 15.4.4.19 Array.prototype.map(callbackfn [, thisArg]) map: function map(callbackfn /* , thisArg */){ return $map(this, callbackfn, arguments[1]); } }); /***/ }), /* 183 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $filter = __webpack_require__(179)(2); $export($export.P + $export.F * !__webpack_require__(175)([].filter, true), 'Array', { // 22.1.3.7 / 15.4.4.20 Array.prototype.filter(callbackfn [, thisArg]) filter: function filter(callbackfn /* , thisArg */){ return $filter(this, callbackfn, arguments[1]); } }); /***/ }), /* 184 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $some = __webpack_require__(179)(3); $export($export.P + $export.F * !__webpack_require__(175)([].some, true), 'Array', { // 22.1.3.23 / 15.4.4.17 Array.prototype.some(callbackfn [, thisArg]) some: function some(callbackfn /* , thisArg */){ return $some(this, callbackfn, arguments[1]); } }); /***/ }), /* 185 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $every = __webpack_require__(179)(4); $export($export.P + $export.F * !__webpack_require__(175)([].every, true), 'Array', { // 22.1.3.5 / 15.4.4.16 Array.prototype.every(callbackfn [, thisArg]) every: function every(callbackfn /* , thisArg */){ return $every(this, callbackfn, arguments[1]); } }); /***/ }), /* 186 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $reduce = __webpack_require__(187); $export($export.P + $export.F * !__webpack_require__(175)([].reduce, true), 'Array', { // 22.1.3.18 / 15.4.4.21 Array.prototype.reduce(callbackfn [, initialValue]) reduce: function reduce(callbackfn /* , initialValue */){ return $reduce(this, callbackfn, arguments.length, arguments[1], false); } }); /***/ }), /* 187 */ /***/ (function(module, exports, __webpack_require__) { var aFunction = __webpack_require__(28) , toObject = __webpack_require__(65) , IObject = __webpack_require__(40) , toLength = __webpack_require__(44); module.exports = function(that, callbackfn, aLen, memo, isRight){ aFunction(callbackfn); var O = toObject(that) , self = IObject(O) , length = toLength(O.length) , index = isRight ? length - 1 : 0 , i = isRight ? -1 : 1; if(aLen < 2)for(;;){ if(index in self){ memo = self[index]; index += i; break; } index += i; if(isRight ? index < 0 : length <= index){ throw TypeError('Reduce of empty array with no initial value'); } } for(;isRight ? index >= 0 : length > index; index += i)if(index in self){ memo = callbackfn(memo, self[index], index, O); } return memo; }; /***/ }), /* 188 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $reduce = __webpack_require__(187); $export($export.P + $export.F * !__webpack_require__(175)([].reduceRight, true), 'Array', { // 22.1.3.19 / 15.4.4.22 Array.prototype.reduceRight(callbackfn [, initialValue]) reduceRight: function reduceRight(callbackfn /* , initialValue */){ return $reduce(this, callbackfn, arguments.length, arguments[1], true); } }); /***/ }), /* 189 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $indexOf = __webpack_require__(43)(false) , $native = [].indexOf , NEGATIVE_ZERO = !!$native && 1 / [1].indexOf(1, -0) < 0; $export($export.P + $export.F * (NEGATIVE_ZERO || !__webpack_require__(175)($native)), 'Array', { // 22.1.3.11 / 15.4.4.14 Array.prototype.indexOf(searchElement [, fromIndex]) indexOf: function indexOf(searchElement /*, fromIndex = 0 */){ return NEGATIVE_ZERO // convert -0 to +0 ? $native.apply(this, arguments) || 0 : $indexOf(this, searchElement, arguments[1]); } }); /***/ }), /* 190 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toIObject = __webpack_require__(39) , toInteger = __webpack_require__(45) , toLength = __webpack_require__(44) , $native = [].lastIndexOf , NEGATIVE_ZERO = !!$native && 1 / [1].lastIndexOf(1, -0) < 0; $export($export.P + $export.F * (NEGATIVE_ZERO || !__webpack_require__(175)($native)), 'Array', { // 22.1.3.14 / 15.4.4.15 Array.prototype.lastIndexOf(searchElement [, fromIndex]) lastIndexOf: function lastIndexOf(searchElement /*, fromIndex = @[*-1] */){ // convert -0 to +0 if(NEGATIVE_ZERO)return $native.apply(this, arguments) || 0; var O = toIObject(this) , length = toLength(O.length) , index = length - 1; if(arguments.length > 1)index = Math.min(index, toInteger(arguments[1])); if(index < 0)index = length + index; for(;index >= 0; index--)if(index in O)if(O[index] === searchElement)return index || 0; return -1; } }); /***/ }), /* 191 */ /***/ (function(module, exports, __webpack_require__) { // 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length) var $export = __webpack_require__(15); $export($export.P, 'Array', {copyWithin: __webpack_require__(192)}); __webpack_require__(193)('copyWithin'); /***/ }), /* 192 */ /***/ (function(module, exports, __webpack_require__) { // 22.1.3.3 Array.prototype.copyWithin(target, start, end = this.length) 'use strict'; var toObject = __webpack_require__(65) , toIndex = __webpack_require__(46) , toLength = __webpack_require__(44); module.exports = [].copyWithin || function copyWithin(target/*= 0*/, start/*= 0, end = @length*/){ var O = toObject(this) , len = toLength(O.length) , to = toIndex(target, len) , from = toIndex(start, len) , end = arguments.length > 2 ? arguments[2] : undefined , count = Math.min((end === undefined ? len : toIndex(end, len)) - from, len - to) , inc = 1; if(from < to && to < from + count){ inc = -1; from += count - 1; to += count - 1; } while(count-- > 0){ if(from in O)O[to] = O[from]; else delete O[to]; to += inc; from += inc; } return O; }; /***/ }), /* 193 */ /***/ (function(module, exports, __webpack_require__) { // 22.1.3.31 Array.prototype[@@unscopables] var UNSCOPABLES = __webpack_require__(32)('unscopables') , ArrayProto = Array.prototype; if(ArrayProto[UNSCOPABLES] == undefined)__webpack_require__(17)(ArrayProto, UNSCOPABLES, {}); module.exports = function(key){ ArrayProto[UNSCOPABLES][key] = true; }; /***/ }), /* 194 */ /***/ (function(module, exports, __webpack_require__) { // 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length) var $export = __webpack_require__(15); $export($export.P, 'Array', {fill: __webpack_require__(195)}); __webpack_require__(193)('fill'); /***/ }), /* 195 */ /***/ (function(module, exports, __webpack_require__) { // 22.1.3.6 Array.prototype.fill(value, start = 0, end = this.length) 'use strict'; var toObject = __webpack_require__(65) , toIndex = __webpack_require__(46) , toLength = __webpack_require__(44); module.exports = function fill(value /*, start = 0, end = @length */){ var O = toObject(this) , length = toLength(O.length) , aLen = arguments.length , index = toIndex(aLen > 1 ? arguments[1] : undefined, length) , end = aLen > 2 ? arguments[2] : undefined , endPos = end === undefined ? length : toIndex(end, length); while(endPos > index)O[index++] = value; return O; }; /***/ }), /* 196 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 22.1.3.8 Array.prototype.find(predicate, thisArg = undefined) var $export = __webpack_require__(15) , $find = __webpack_require__(179)(5) , KEY = 'find' , forced = true; // Shouldn't skip holes if(KEY in [])Array(1)[KEY](function(){ forced = false; }); $export($export.P + $export.F * forced, 'Array', { find: function find(callbackfn/*, that = undefined */){ return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined); } }); __webpack_require__(193)(KEY); /***/ }), /* 197 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 22.1.3.9 Array.prototype.findIndex(predicate, thisArg = undefined) var $export = __webpack_require__(15) , $find = __webpack_require__(179)(6) , KEY = 'findIndex' , forced = true; // Shouldn't skip holes if(KEY in [])Array(1)[KEY](function(){ forced = false; }); $export($export.P + $export.F * forced, 'Array', { findIndex: function findIndex(callbackfn/*, that = undefined */){ return $find(this, callbackfn, arguments.length > 1 ? arguments[1] : undefined); } }); __webpack_require__(193)(KEY); /***/ }), /* 198 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(199)('Array'); /***/ }), /* 199 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var global = __webpack_require__(11) , dP = __webpack_require__(18) , DESCRIPTORS = __webpack_require__(13) , SPECIES = __webpack_require__(32)('species'); module.exports = function(KEY){ var C = global[KEY]; if(DESCRIPTORS && C && !C[SPECIES])dP.f(C, SPECIES, { configurable: true, get: function(){ return this; } }); }; /***/ }), /* 200 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var addToUnscopables = __webpack_require__(193) , step = __webpack_require__(201) , Iterators = __webpack_require__(136) , toIObject = __webpack_require__(39); // 22.1.3.4 Array.prototype.entries() // 22.1.3.13 Array.prototype.keys() // 22.1.3.29 Array.prototype.values() // 22.1.3.30 Array.prototype[@@iterator]() module.exports = __webpack_require__(135)(Array, 'Array', function(iterated, kind){ this._t = toIObject(iterated); // target this._i = 0; // next index this._k = kind; // kind // 22.1.5.2.1 %ArrayIteratorPrototype%.next() }, function(){ var O = this._t , kind = this._k , index = this._i++; if(!O || index >= O.length){ this._t = undefined; return step(1); } if(kind == 'keys' )return step(0, index); if(kind == 'values')return step(0, O[index]); return step(0, [index, O[index]]); }, 'values'); // argumentsList[@@iterator] is %ArrayProto_values% (9.4.4.6, 9.4.4.7) Iterators.Arguments = Iterators.Array; addToUnscopables('keys'); addToUnscopables('values'); addToUnscopables('entries'); /***/ }), /* 201 */ /***/ (function(module, exports) { module.exports = function(done, value){ return {value: value, done: !!done}; }; /***/ }), /* 202 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , inheritIfRequired = __webpack_require__(95) , dP = __webpack_require__(18).f , gOPN = __webpack_require__(57).f , isRegExp = __webpack_require__(141) , $flags = __webpack_require__(203) , $RegExp = global.RegExp , Base = $RegExp , proto = $RegExp.prototype , re1 = /a/g , re2 = /a/g // "new" creates a new object, old webkit buggy here , CORRECT_NEW = new $RegExp(re1) !== re1; if(__webpack_require__(13) && (!CORRECT_NEW || __webpack_require__(14)(function(){ re2[__webpack_require__(32)('match')] = false; // RegExp constructor can alter flags and IsRegExp works correct with @@match return $RegExp(re1) != re1 || $RegExp(re2) == re2 || $RegExp(re1, 'i') != '/a/i'; }))){ $RegExp = function RegExp(p, f){ var tiRE = this instanceof $RegExp , piRE = isRegExp(p) , fiU = f === undefined; return !tiRE && piRE && p.constructor === $RegExp && fiU ? p : inheritIfRequired(CORRECT_NEW ? new Base(piRE && !fiU ? p.source : p, f) : Base((piRE = p instanceof $RegExp) ? p.source : p, piRE && fiU ? $flags.call(p) : f) , tiRE ? this : proto, $RegExp); }; var proxy = function(key){ key in $RegExp || dP($RegExp, key, { configurable: true, get: function(){ return Base[key]; }, set: function(it){ Base[key] = it; } }); }; for(var keys = gOPN(Base), i = 0; keys.length > i; )proxy(keys[i++]); proto.constructor = $RegExp; $RegExp.prototype = proto; __webpack_require__(25)(global, 'RegExp', $RegExp); } __webpack_require__(199)('RegExp'); /***/ }), /* 203 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 21.2.5.3 get RegExp.prototype.flags var anObject = __webpack_require__(19); module.exports = function(){ var that = anObject(this) , result = ''; if(that.global) result += 'g'; if(that.ignoreCase) result += 'i'; if(that.multiline) result += 'm'; if(that.unicode) result += 'u'; if(that.sticky) result += 'y'; return result; }; /***/ }), /* 204 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; __webpack_require__(205); var anObject = __webpack_require__(19) , $flags = __webpack_require__(203) , DESCRIPTORS = __webpack_require__(13) , TO_STRING = 'toString' , $toString = /./[TO_STRING]; var define = function(fn){ __webpack_require__(25)(RegExp.prototype, TO_STRING, fn, true); }; // 21.2.5.14 RegExp.prototype.toString() if(__webpack_require__(14)(function(){ return $toString.call({source: 'a', flags: 'b'}) != '/a/b'; })){ define(function toString(){ var R = anObject(this); return '/'.concat(R.source, '/', 'flags' in R ? R.flags : !DESCRIPTORS && R instanceof RegExp ? $flags.call(R) : undefined); }); // FF44- RegExp#toString has a wrong name } else if($toString.name != TO_STRING){ define(function toString(){ return $toString.call(this); }); } /***/ }), /* 205 */ /***/ (function(module, exports, __webpack_require__) { // 21.2.5.3 get RegExp.prototype.flags() if(__webpack_require__(13) && /./g.flags != 'g')__webpack_require__(18).f(RegExp.prototype, 'flags', { configurable: true, get: __webpack_require__(203) }); /***/ }), /* 206 */ /***/ (function(module, exports, __webpack_require__) { // @@match logic __webpack_require__(207)('match', 1, function(defined, MATCH, $match){ // 21.1.3.11 String.prototype.match(regexp) return [function match(regexp){ 'use strict'; var O = defined(this) , fn = regexp == undefined ? undefined : regexp[MATCH]; return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[MATCH](String(O)); }, $match]; }); /***/ }), /* 207 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var hide = __webpack_require__(17) , redefine = __webpack_require__(25) , fails = __webpack_require__(14) , defined = __webpack_require__(42) , wks = __webpack_require__(32); module.exports = function(KEY, length, exec){ var SYMBOL = wks(KEY) , fns = exec(defined, SYMBOL, ''[KEY]) , strfn = fns[0] , rxfn = fns[1]; if(fails(function(){ var O = {}; O[SYMBOL] = function(){ return 7; }; return ''[KEY](O) != 7; })){ redefine(String.prototype, KEY, strfn); hide(RegExp.prototype, SYMBOL, length == 2 // 21.2.5.8 RegExp.prototype[@@replace](string, replaceValue) // 21.2.5.11 RegExp.prototype[@@split](string, limit) ? function(string, arg){ return rxfn.call(string, this, arg); } // 21.2.5.6 RegExp.prototype[@@match](string) // 21.2.5.9 RegExp.prototype[@@search](string) : function(string){ return rxfn.call(string, this); } ); } }; /***/ }), /* 208 */ /***/ (function(module, exports, __webpack_require__) { // @@replace logic __webpack_require__(207)('replace', 2, function(defined, REPLACE, $replace){ // 21.1.3.14 String.prototype.replace(searchValue, replaceValue) return [function replace(searchValue, replaceValue){ 'use strict'; var O = defined(this) , fn = searchValue == undefined ? undefined : searchValue[REPLACE]; return fn !== undefined ? fn.call(searchValue, O, replaceValue) : $replace.call(String(O), searchValue, replaceValue); }, $replace]; }); /***/ }), /* 209 */ /***/ (function(module, exports, __webpack_require__) { // @@search logic __webpack_require__(207)('search', 1, function(defined, SEARCH, $search){ // 21.1.3.15 String.prototype.search(regexp) return [function search(regexp){ 'use strict'; var O = defined(this) , fn = regexp == undefined ? undefined : regexp[SEARCH]; return fn !== undefined ? fn.call(regexp, O) : new RegExp(regexp)[SEARCH](String(O)); }, $search]; }); /***/ }), /* 210 */ /***/ (function(module, exports, __webpack_require__) { // @@split logic __webpack_require__(207)('split', 2, function(defined, SPLIT, $split){ 'use strict'; var isRegExp = __webpack_require__(141) , _split = $split , $push = [].push , $SPLIT = 'split' , LENGTH = 'length' , LAST_INDEX = 'lastIndex'; if( 'abbc'[$SPLIT](/(b)*/)[1] == 'c' || 'test'[$SPLIT](/(?:)/, -1)[LENGTH] != 4 || 'ab'[$SPLIT](/(?:ab)*/)[LENGTH] != 2 || '.'[$SPLIT](/(.?)(.?)/)[LENGTH] != 4 || '.'[$SPLIT](/()()/)[LENGTH] > 1 || ''[$SPLIT](/.?/)[LENGTH] ){ var NPCG = /()??/.exec('')[1] === undefined; // nonparticipating capturing group // based on es5-shim implementation, need to rework it $split = function(separator, limit){ var string = String(this); if(separator === undefined && limit === 0)return []; // If `separator` is not a regex, use native split if(!isRegExp(separator))return _split.call(string, separator, limit); var output = []; var flags = (separator.ignoreCase ? 'i' : '') + (separator.multiline ? 'm' : '') + (separator.unicode ? 'u' : '') + (separator.sticky ? 'y' : ''); var lastLastIndex = 0; var splitLimit = limit === undefined ? 4294967295 : limit >>> 0; // Make `global` and avoid `lastIndex` issues by working with a copy var separatorCopy = new RegExp(separator.source, flags + 'g'); var separator2, match, lastIndex, lastLength, i; // Doesn't need flags gy, but they don't hurt if(!NPCG)separator2 = new RegExp('^' + separatorCopy.source + '$(?!\\s)', flags); while(match = separatorCopy.exec(string)){ // `separatorCopy.lastIndex` is not reliable cross-browser lastIndex = match.index + match[0][LENGTH]; if(lastIndex > lastLastIndex){ output.push(string.slice(lastLastIndex, match.index)); // Fix browsers whose `exec` methods don't consistently return `undefined` for NPCG if(!NPCG && match[LENGTH] > 1)match[0].replace(separator2, function(){ for(i = 1; i < arguments[LENGTH] - 2; i++)if(arguments[i] === undefined)match[i] = undefined; }); if(match[LENGTH] > 1 && match.index < string[LENGTH])$push.apply(output, match.slice(1)); lastLength = match[0][LENGTH]; lastLastIndex = lastIndex; if(output[LENGTH] >= splitLimit)break; } if(separatorCopy[LAST_INDEX] === match.index)separatorCopy[LAST_INDEX]++; // Avoid an infinite loop } if(lastLastIndex === string[LENGTH]){ if(lastLength || !separatorCopy.test(''))output.push(''); } else output.push(string.slice(lastLastIndex)); return output[LENGTH] > splitLimit ? output.slice(0, splitLimit) : output; }; // Chakra, V8 } else if('0'[$SPLIT](undefined, 0)[LENGTH]){ $split = function(separator, limit){ return separator === undefined && limit === 0 ? [] : _split.call(this, separator, limit); }; } // 21.1.3.17 String.prototype.split(separator, limit) return [function split(separator, limit){ var O = defined(this) , fn = separator == undefined ? undefined : separator[SPLIT]; return fn !== undefined ? fn.call(separator, O, limit) : $split.call(String(O), separator, limit); }, $split]; }); /***/ }), /* 211 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var LIBRARY = __webpack_require__(35) , global = __webpack_require__(11) , ctx = __webpack_require__(27) , classof = __webpack_require__(82) , $export = __webpack_require__(15) , isObject = __webpack_require__(20) , aFunction = __webpack_require__(28) , anInstance = __webpack_require__(212) , forOf = __webpack_require__(213) , speciesConstructor = __webpack_require__(214) , task = __webpack_require__(215).set , microtask = __webpack_require__(216)() , PROMISE = 'Promise' , TypeError = global.TypeError , process = global.process , $Promise = global[PROMISE] , process = global.process , isNode = classof(process) == 'process' , empty = function(){ /* empty */ } , Internal, GenericPromiseCapability, Wrapper; var USE_NATIVE = !!function(){ try { // correct subclassing with @@species support var promise = $Promise.resolve(1) , FakePromise = (promise.constructor = {})[__webpack_require__(32)('species')] = function(exec){ exec(empty, empty); }; // unhandled rejections tracking support, NodeJS Promise without it fails @@species test return (isNode || typeof PromiseRejectionEvent == 'function') && promise.then(empty) instanceof FakePromise; } catch(e){ /* empty */ } }(); // helpers var sameConstructor = function(a, b){ // with library wrapper special case return a === b || a === $Promise && b === Wrapper; }; var isThenable = function(it){ var then; return isObject(it) && typeof (then = it.then) == 'function' ? then : false; }; var newPromiseCapability = function(C){ return sameConstructor($Promise, C) ? new PromiseCapability(C) : new GenericPromiseCapability(C); }; var PromiseCapability = GenericPromiseCapability = function(C){ var resolve, reject; this.promise = new C(function($$resolve, $$reject){ if(resolve !== undefined || reject !== undefined)throw TypeError('Bad Promise constructor'); resolve = $$resolve; reject = $$reject; }); this.resolve = aFunction(resolve); this.reject = aFunction(reject); }; var perform = function(exec){ try { exec(); } catch(e){ return {error: e}; } }; var notify = function(promise, isReject){ if(promise._n)return; promise._n = true; var chain = promise._c; microtask(function(){ var value = promise._v , ok = promise._s == 1 , i = 0; var run = function(reaction){ var handler = ok ? reaction.ok : reaction.fail , resolve = reaction.resolve , reject = reaction.reject , domain = reaction.domain , result, then; try { if(handler){ if(!ok){ if(promise._h == 2)onHandleUnhandled(promise); promise._h = 1; } if(handler === true)result = value; else { if(domain)domain.enter(); result = handler(value); if(domain)domain.exit(); } if(result === reaction.promise){ reject(TypeError('Promise-chain cycle')); } else if(then = isThenable(result)){ then.call(result, resolve, reject); } else resolve(result); } else reject(value); } catch(e){ reject(e); } }; while(chain.length > i)run(chain[i++]); // variable length - can't use forEach promise._c = []; promise._n = false; if(isReject && !promise._h)onUnhandled(promise); }); }; var onUnhandled = function(promise){ task.call(global, function(){ var value = promise._v , abrupt, handler, console; if(isUnhandled(promise)){ abrupt = perform(function(){ if(isNode){ process.emit('unhandledRejection', value, promise); } else if(handler = global.onunhandledrejection){ handler({promise: promise, reason: value}); } else if((console = global.console) && console.error){ console.error('Unhandled promise rejection', value); } }); // Browsers should not trigger `rejectionHandled` event if it was handled here, NodeJS - should promise._h = isNode || isUnhandled(promise) ? 2 : 1; } promise._a = undefined; if(abrupt)throw abrupt.error; }); }; var isUnhandled = function(promise){ if(promise._h == 1)return false; var chain = promise._a || promise._c , i = 0 , reaction; while(chain.length > i){ reaction = chain[i++]; if(reaction.fail || !isUnhandled(reaction.promise))return false; } return true; }; var onHandleUnhandled = function(promise){ task.call(global, function(){ var handler; if(isNode){ process.emit('rejectionHandled', promise); } else if(handler = global.onrejectionhandled){ handler({promise: promise, reason: promise._v}); } }); }; var $reject = function(value){ var promise = this; if(promise._d)return; promise._d = true; promise = promise._w || promise; // unwrap promise._v = value; promise._s = 2; if(!promise._a)promise._a = promise._c.slice(); notify(promise, true); }; var $resolve = function(value){ var promise = this , then; if(promise._d)return; promise._d = true; promise = promise._w || promise; // unwrap try { if(promise === value)throw TypeError("Promise can't be resolved itself"); if(then = isThenable(value)){ microtask(function(){ var wrapper = {_w: promise, _d: false}; // wrap try { then.call(value, ctx($resolve, wrapper, 1), ctx($reject, wrapper, 1)); } catch(e){ $reject.call(wrapper, e); } }); } else { promise._v = value; promise._s = 1; notify(promise, false); } } catch(e){ $reject.call({_w: promise, _d: false}, e); // wrap } }; // constructor polyfill if(!USE_NATIVE){ // 25.4.3.1 Promise(executor) $Promise = function Promise(executor){ anInstance(this, $Promise, PROMISE, '_h'); aFunction(executor); Internal.call(this); try { executor(ctx($resolve, this, 1), ctx($reject, this, 1)); } catch(err){ $reject.call(this, err); } }; Internal = function Promise(executor){ this._c = []; // <- awaiting reactions this._a = undefined; // <- checked in isUnhandled reactions this._s = 0; // <- state this._d = false; // <- done this._v = undefined; // <- value this._h = 0; // <- rejection state, 0 - default, 1 - handled, 2 - unhandled this._n = false; // <- notify }; Internal.prototype = __webpack_require__(217)($Promise.prototype, { // 25.4.5.3 Promise.prototype.then(onFulfilled, onRejected) then: function then(onFulfilled, onRejected){ var reaction = newPromiseCapability(speciesConstructor(this, $Promise)); reaction.ok = typeof onFulfilled == 'function' ? onFulfilled : true; reaction.fail = typeof onRejected == 'function' && onRejected; reaction.domain = isNode ? process.domain : undefined; this._c.push(reaction); if(this._a)this._a.push(reaction); if(this._s)notify(this, false); return reaction.promise; }, // 25.4.5.1 Promise.prototype.catch(onRejected) 'catch': function(onRejected){ return this.then(undefined, onRejected); } }); PromiseCapability = function(){ var promise = new Internal; this.promise = promise; this.resolve = ctx($resolve, promise, 1); this.reject = ctx($reject, promise, 1); }; } $export($export.G + $export.W + $export.F * !USE_NATIVE, {Promise: $Promise}); __webpack_require__(31)($Promise, PROMISE); __webpack_require__(199)(PROMISE); Wrapper = __webpack_require__(16)[PROMISE]; // statics $export($export.S + $export.F * !USE_NATIVE, PROMISE, { // 25.4.4.5 Promise.reject(r) reject: function reject(r){ var capability = newPromiseCapability(this) , $$reject = capability.reject; $$reject(r); return capability.promise; } }); $export($export.S + $export.F * (LIBRARY || !USE_NATIVE), PROMISE, { // 25.4.4.6 Promise.resolve(x) resolve: function resolve(x){ // instanceof instead of internal slot check because we should fix it without replacement native Promise core if(x instanceof $Promise && sameConstructor(x.constructor, this))return x; var capability = newPromiseCapability(this) , $$resolve = capability.resolve; $$resolve(x); return capability.promise; } }); $export($export.S + $export.F * !(USE_NATIVE && __webpack_require__(172)(function(iter){ $Promise.all(iter)['catch'](empty); })), PROMISE, { // 25.4.4.1 Promise.all(iterable) all: function all(iterable){ var C = this , capability = newPromiseCapability(C) , resolve = capability.resolve , reject = capability.reject; var abrupt = perform(function(){ var values = [] , index = 0 , remaining = 1; forOf(iterable, false, function(promise){ var $index = index++ , alreadyCalled = false; values.push(undefined); remaining++; C.resolve(promise).then(function(value){ if(alreadyCalled)return; alreadyCalled = true; values[$index] = value; --remaining || resolve(values); }, reject); }); --remaining || resolve(values); }); if(abrupt)reject(abrupt.error); return capability.promise; }, // 25.4.4.4 Promise.race(iterable) race: function race(iterable){ var C = this , capability = newPromiseCapability(C) , reject = capability.reject; var abrupt = perform(function(){ forOf(iterable, false, function(promise){ C.resolve(promise).then(capability.resolve, reject); }); }); if(abrupt)reject(abrupt.error); return capability.promise; } }); /***/ }), /* 212 */ /***/ (function(module, exports) { module.exports = function(it, Constructor, name, forbiddenField){ if(!(it instanceof Constructor) || (forbiddenField !== undefined && forbiddenField in it)){ throw TypeError(name + ': incorrect invocation!'); } return it; }; /***/ }), /* 213 */ /***/ (function(module, exports, __webpack_require__) { var ctx = __webpack_require__(27) , call = __webpack_require__(168) , isArrayIter = __webpack_require__(169) , anObject = __webpack_require__(19) , toLength = __webpack_require__(44) , getIterFn = __webpack_require__(171) , BREAK = {} , RETURN = {}; var exports = module.exports = function(iterable, entries, fn, that, ITERATOR){ var iterFn = ITERATOR ? function(){ return iterable; } : getIterFn(iterable) , f = ctx(fn, that, entries ? 2 : 1) , index = 0 , length, step, iterator, result; if(typeof iterFn != 'function')throw TypeError(iterable + ' is not iterable!'); // fast case for arrays with default iterator if(isArrayIter(iterFn))for(length = toLength(iterable.length); length > index; index++){ result = entries ? f(anObject(step = iterable[index])[0], step[1]) : f(iterable[index]); if(result === BREAK || result === RETURN)return result; } else for(iterator = iterFn.call(iterable); !(step = iterator.next()).done; ){ result = call(iterator, f, step.value, entries); if(result === BREAK || result === RETURN)return result; } }; exports.BREAK = BREAK; exports.RETURN = RETURN; /***/ }), /* 214 */ /***/ (function(module, exports, __webpack_require__) { // 7.3.20 SpeciesConstructor(O, defaultConstructor) var anObject = __webpack_require__(19) , aFunction = __webpack_require__(28) , SPECIES = __webpack_require__(32)('species'); module.exports = function(O, D){ var C = anObject(O).constructor, S; return C === undefined || (S = anObject(C)[SPECIES]) == undefined ? D : aFunction(S); }; /***/ }), /* 215 */ /***/ (function(module, exports, __webpack_require__) { var ctx = __webpack_require__(27) , invoke = __webpack_require__(85) , html = __webpack_require__(55) , cel = __webpack_require__(22) , global = __webpack_require__(11) , process = global.process , setTask = global.setImmediate , clearTask = global.clearImmediate , MessageChannel = global.MessageChannel , counter = 0 , queue = {} , ONREADYSTATECHANGE = 'onreadystatechange' , defer, channel, port; var run = function(){ var id = +this; if(queue.hasOwnProperty(id)){ var fn = queue[id]; delete queue[id]; fn(); } }; var listener = function(event){ run.call(event.data); }; // Node.js 0.9+ & IE10+ has setImmediate, otherwise: if(!setTask || !clearTask){ setTask = function setImmediate(fn){ var args = [], i = 1; while(arguments.length > i)args.push(arguments[i++]); queue[++counter] = function(){ invoke(typeof fn == 'function' ? fn : Function(fn), args); }; defer(counter); return counter; }; clearTask = function clearImmediate(id){ delete queue[id]; }; // Node.js 0.8- if(__webpack_require__(41)(process) == 'process'){ defer = function(id){ process.nextTick(ctx(run, id, 1)); }; // Browsers with MessageChannel, includes WebWorkers } else if(MessageChannel){ channel = new MessageChannel; port = channel.port2; channel.port1.onmessage = listener; defer = ctx(port.postMessage, port, 1); // Browsers with postMessage, skip WebWorkers // IE8 has postMessage, but it's sync & typeof its postMessage is 'object' } else if(global.addEventListener && typeof postMessage == 'function' && !global.importScripts){ defer = function(id){ global.postMessage(id + '', '*'); }; global.addEventListener('message', listener, false); // IE8- } else if(ONREADYSTATECHANGE in cel('script')){ defer = function(id){ html.appendChild(cel('script'))[ONREADYSTATECHANGE] = function(){ html.removeChild(this); run.call(id); }; }; // Rest old browsers } else { defer = function(id){ setTimeout(ctx(run, id, 1), 0); }; } } module.exports = { set: setTask, clear: clearTask }; /***/ }), /* 216 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , macrotask = __webpack_require__(215).set , Observer = global.MutationObserver || global.WebKitMutationObserver , process = global.process , Promise = global.Promise , isNode = __webpack_require__(41)(process) == 'process'; module.exports = function(){ var head, last, notify; var flush = function(){ var parent, fn; if(isNode && (parent = process.domain))parent.exit(); while(head){ fn = head.fn; head = head.next; try { fn(); } catch(e){ if(head)notify(); else last = undefined; throw e; } } last = undefined; if(parent)parent.enter(); }; // Node.js if(isNode){ notify = function(){ process.nextTick(flush); }; // browsers with MutationObserver } else if(Observer){ var toggle = true , node = document.createTextNode(''); new Observer(flush).observe(node, {characterData: true}); // eslint-disable-line no-new notify = function(){ node.data = toggle = !toggle; }; // environments with maybe non-completely correct, but existent Promise } else if(Promise && Promise.resolve){ var promise = Promise.resolve(); notify = function(){ promise.then(flush); }; // for other environments - macrotask based on: // - setImmediate // - MessageChannel // - window.postMessag // - onreadystatechange // - setTimeout } else { notify = function(){ // strange IE + webpack dev server bug - use .call(global) macrotask.call(global, flush); }; } return function(fn){ var task = {fn: fn, next: undefined}; if(last)last.next = task; if(!head){ head = task; notify(); } last = task; }; }; /***/ }), /* 217 */ /***/ (function(module, exports, __webpack_require__) { var redefine = __webpack_require__(25); module.exports = function(target, src, safe){ for(var key in src)redefine(target, key, src[key], safe); return target; }; /***/ }), /* 218 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var strong = __webpack_require__(219); // 23.1 Map Objects module.exports = __webpack_require__(220)('Map', function(get){ return function Map(){ return get(this, arguments.length > 0 ? arguments[0] : undefined); }; }, { // 23.1.3.6 Map.prototype.get(key) get: function get(key){ var entry = strong.getEntry(this, key); return entry && entry.v; }, // 23.1.3.9 Map.prototype.set(key, value) set: function set(key, value){ return strong.def(this, key === 0 ? 0 : key, value); } }, strong, true); /***/ }), /* 219 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var dP = __webpack_require__(18).f , create = __webpack_require__(53) , redefineAll = __webpack_require__(217) , ctx = __webpack_require__(27) , anInstance = __webpack_require__(212) , defined = __webpack_require__(42) , forOf = __webpack_require__(213) , $iterDefine = __webpack_require__(135) , step = __webpack_require__(201) , setSpecies = __webpack_require__(199) , DESCRIPTORS = __webpack_require__(13) , fastKey = __webpack_require__(29).fastKey , SIZE = DESCRIPTORS ? '_s' : 'size'; var getEntry = function(that, key){ // fast case var index = fastKey(key), entry; if(index !== 'F')return that._i[index]; // frozen object case for(entry = that._f; entry; entry = entry.n){ if(entry.k == key)return entry; } }; module.exports = { getConstructor: function(wrapper, NAME, IS_MAP, ADDER){ var C = wrapper(function(that, iterable){ anInstance(that, C, NAME, '_i'); that._i = create(null); // index that._f = undefined; // first entry that._l = undefined; // last entry that[SIZE] = 0; // size if(iterable != undefined)forOf(iterable, IS_MAP, that[ADDER], that); }); redefineAll(C.prototype, { // 23.1.3.1 Map.prototype.clear() // 23.2.3.2 Set.prototype.clear() clear: function clear(){ for(var that = this, data = that._i, entry = that._f; entry; entry = entry.n){ entry.r = true; if(entry.p)entry.p = entry.p.n = undefined; delete data[entry.i]; } that._f = that._l = undefined; that[SIZE] = 0; }, // 23.1.3.3 Map.prototype.delete(key) // 23.2.3.4 Set.prototype.delete(value) 'delete': function(key){ var that = this , entry = getEntry(that, key); if(entry){ var next = entry.n , prev = entry.p; delete that._i[entry.i]; entry.r = true; if(prev)prev.n = next; if(next)next.p = prev; if(that._f == entry)that._f = next; if(that._l == entry)that._l = prev; that[SIZE]--; } return !!entry; }, // 23.2.3.6 Set.prototype.forEach(callbackfn, thisArg = undefined) // 23.1.3.5 Map.prototype.forEach(callbackfn, thisArg = undefined) forEach: function forEach(callbackfn /*, that = undefined */){ anInstance(this, C, 'forEach'); var f = ctx(callbackfn, arguments.length > 1 ? arguments[1] : undefined, 3) , entry; while(entry = entry ? entry.n : this._f){ f(entry.v, entry.k, this); // revert to the last existing entry while(entry && entry.r)entry = entry.p; } }, // 23.1.3.7 Map.prototype.has(key) // 23.2.3.7 Set.prototype.has(value) has: function has(key){ return !!getEntry(this, key); } }); if(DESCRIPTORS)dP(C.prototype, 'size', { get: function(){ return defined(this[SIZE]); } }); return C; }, def: function(that, key, value){ var entry = getEntry(that, key) , prev, index; // change existing entry if(entry){ entry.v = value; // create new entry } else { that._l = entry = { i: index = fastKey(key, true), // <- index k: key, // <- key v: value, // <- value p: prev = that._l, // <- previous entry n: undefined, // <- next entry r: false // <- removed }; if(!that._f)that._f = entry; if(prev)prev.n = entry; that[SIZE]++; // add to index if(index !== 'F')that._i[index] = entry; } return that; }, getEntry: getEntry, setStrong: function(C, NAME, IS_MAP){ // add .keys, .values, .entries, [@@iterator] // 23.1.3.4, 23.1.3.8, 23.1.3.11, 23.1.3.12, 23.2.3.5, 23.2.3.8, 23.2.3.10, 23.2.3.11 $iterDefine(C, NAME, function(iterated, kind){ this._t = iterated; // target this._k = kind; // kind this._l = undefined; // previous }, function(){ var that = this , kind = that._k , entry = that._l; // revert to the last existing entry while(entry && entry.r)entry = entry.p; // get next entry if(!that._t || !(that._l = entry = entry ? entry.n : that._t._f)){ // or finish the iteration that._t = undefined; return step(1); } // return step by kind if(kind == 'keys' )return step(0, entry.k); if(kind == 'values')return step(0, entry.v); return step(0, [entry.k, entry.v]); }, IS_MAP ? 'entries' : 'values' , !IS_MAP, true); // add [@@species], 23.1.2.2, 23.2.2.2 setSpecies(NAME); } }; /***/ }), /* 220 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var global = __webpack_require__(11) , $export = __webpack_require__(15) , redefine = __webpack_require__(25) , redefineAll = __webpack_require__(217) , meta = __webpack_require__(29) , forOf = __webpack_require__(213) , anInstance = __webpack_require__(212) , isObject = __webpack_require__(20) , fails = __webpack_require__(14) , $iterDetect = __webpack_require__(172) , setToStringTag = __webpack_require__(31) , inheritIfRequired = __webpack_require__(95); module.exports = function(NAME, wrapper, methods, common, IS_MAP, IS_WEAK){ var Base = global[NAME] , C = Base , ADDER = IS_MAP ? 'set' : 'add' , proto = C && C.prototype , O = {}; var fixMethod = function(KEY){ var fn = proto[KEY]; redefine(proto, KEY, KEY == 'delete' ? function(a){ return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a); } : KEY == 'has' ? function has(a){ return IS_WEAK && !isObject(a) ? false : fn.call(this, a === 0 ? 0 : a); } : KEY == 'get' ? function get(a){ return IS_WEAK && !isObject(a) ? undefined : fn.call(this, a === 0 ? 0 : a); } : KEY == 'add' ? function add(a){ fn.call(this, a === 0 ? 0 : a); return this; } : function set(a, b){ fn.call(this, a === 0 ? 0 : a, b); return this; } ); }; if(typeof C != 'function' || !(IS_WEAK || proto.forEach && !fails(function(){ new C().entries().next(); }))){ // create collection constructor C = common.getConstructor(wrapper, NAME, IS_MAP, ADDER); redefineAll(C.prototype, methods); meta.NEED = true; } else { var instance = new C // early implementations not supports chaining , HASNT_CHAINING = instance[ADDER](IS_WEAK ? {} : -0, 1) != instance // V8 ~ Chromium 40- weak-collections throws on primitives, but should return false , THROWS_ON_PRIMITIVES = fails(function(){ instance.has(1); }) // most early implementations doesn't supports iterables, most modern - not close it correctly , ACCEPT_ITERABLES = $iterDetect(function(iter){ new C(iter); }) // eslint-disable-line no-new // for early implementations -0 and +0 not the same , BUGGY_ZERO = !IS_WEAK && fails(function(){ // V8 ~ Chromium 42- fails only with 5+ elements var $instance = new C() , index = 5; while(index--)$instance[ADDER](index, index); return !$instance.has(-0); }); if(!ACCEPT_ITERABLES){ C = wrapper(function(target, iterable){ anInstance(target, C, NAME); var that = inheritIfRequired(new Base, target, C); if(iterable != undefined)forOf(iterable, IS_MAP, that[ADDER], that); return that; }); C.prototype = proto; proto.constructor = C; } if(THROWS_ON_PRIMITIVES || BUGGY_ZERO){ fixMethod('delete'); fixMethod('has'); IS_MAP && fixMethod('get'); } if(BUGGY_ZERO || HASNT_CHAINING)fixMethod(ADDER); // weak collections should not contains .clear method if(IS_WEAK && proto.clear)delete proto.clear; } setToStringTag(C, NAME); O[NAME] = C; $export($export.G + $export.W + $export.F * (C != Base), O); if(!IS_WEAK)common.setStrong(C, NAME, IS_MAP); return C; }; /***/ }), /* 221 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var strong = __webpack_require__(219); // 23.2 Set Objects module.exports = __webpack_require__(220)('Set', function(get){ return function Set(){ return get(this, arguments.length > 0 ? arguments[0] : undefined); }; }, { // 23.2.3.1 Set.prototype.add(value) add: function add(value){ return strong.def(this, value = value === 0 ? 0 : value, value); } }, strong); /***/ }), /* 222 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var each = __webpack_require__(179)(0) , redefine = __webpack_require__(25) , meta = __webpack_require__(29) , assign = __webpack_require__(76) , weak = __webpack_require__(223) , isObject = __webpack_require__(20) , getWeak = meta.getWeak , isExtensible = Object.isExtensible , uncaughtFrozenStore = weak.ufstore , tmp = {} , InternalMap; var wrapper = function(get){ return function WeakMap(){ return get(this, arguments.length > 0 ? arguments[0] : undefined); }; }; var methods = { // 23.3.3.3 WeakMap.prototype.get(key) get: function get(key){ if(isObject(key)){ var data = getWeak(key); if(data === true)return uncaughtFrozenStore(this).get(key); return data ? data[this._i] : undefined; } }, // 23.3.3.5 WeakMap.prototype.set(key, value) set: function set(key, value){ return weak.def(this, key, value); } }; // 23.3 WeakMap Objects var $WeakMap = module.exports = __webpack_require__(220)('WeakMap', wrapper, methods, weak, true, true); // IE11 WeakMap frozen keys fix if(new $WeakMap().set((Object.freeze || Object)(tmp), 7).get(tmp) != 7){ InternalMap = weak.getConstructor(wrapper); assign(InternalMap.prototype, methods); meta.NEED = true; each(['delete', 'has', 'get', 'set'], function(key){ var proto = $WeakMap.prototype , method = proto[key]; redefine(proto, key, function(a, b){ // store frozen objects on internal weakmap shim if(isObject(a) && !isExtensible(a)){ if(!this._f)this._f = new InternalMap; var result = this._f[key](a, b); return key == 'set' ? this : result; // store all the rest on native weakmap } return method.call(this, a, b); }); }); } /***/ }), /* 223 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var redefineAll = __webpack_require__(217) , getWeak = __webpack_require__(29).getWeak , anObject = __webpack_require__(19) , isObject = __webpack_require__(20) , anInstance = __webpack_require__(212) , forOf = __webpack_require__(213) , createArrayMethod = __webpack_require__(179) , $has = __webpack_require__(12) , arrayFind = createArrayMethod(5) , arrayFindIndex = createArrayMethod(6) , id = 0; // fallback for uncaught frozen keys var uncaughtFrozenStore = function(that){ return that._l || (that._l = new UncaughtFrozenStore); }; var UncaughtFrozenStore = function(){ this.a = []; }; var findUncaughtFrozen = function(store, key){ return arrayFind(store.a, function(it){ return it[0] === key; }); }; UncaughtFrozenStore.prototype = { get: function(key){ var entry = findUncaughtFrozen(this, key); if(entry)return entry[1]; }, has: function(key){ return !!findUncaughtFrozen(this, key); }, set: function(key, value){ var entry = findUncaughtFrozen(this, key); if(entry)entry[1] = value; else this.a.push([key, value]); }, 'delete': function(key){ var index = arrayFindIndex(this.a, function(it){ return it[0] === key; }); if(~index)this.a.splice(index, 1); return !!~index; } }; module.exports = { getConstructor: function(wrapper, NAME, IS_MAP, ADDER){ var C = wrapper(function(that, iterable){ anInstance(that, C, NAME, '_i'); that._i = id++; // collection id that._l = undefined; // leak store for uncaught frozen objects if(iterable != undefined)forOf(iterable, IS_MAP, that[ADDER], that); }); redefineAll(C.prototype, { // 23.3.3.2 WeakMap.prototype.delete(key) // 23.4.3.3 WeakSet.prototype.delete(value) 'delete': function(key){ if(!isObject(key))return false; var data = getWeak(key); if(data === true)return uncaughtFrozenStore(this)['delete'](key); return data && $has(data, this._i) && delete data[this._i]; }, // 23.3.3.4 WeakMap.prototype.has(key) // 23.4.3.4 WeakSet.prototype.has(value) has: function has(key){ if(!isObject(key))return false; var data = getWeak(key); if(data === true)return uncaughtFrozenStore(this).has(key); return data && $has(data, this._i); } }); return C; }, def: function(that, key, value){ var data = getWeak(anObject(key), true); if(data === true)uncaughtFrozenStore(that).set(key, value); else data[that._i] = value; return that; }, ufstore: uncaughtFrozenStore }; /***/ }), /* 224 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var weak = __webpack_require__(223); // 23.4 WeakSet Objects __webpack_require__(220)('WeakSet', function(get){ return function WeakSet(){ return get(this, arguments.length > 0 ? arguments[0] : undefined); }; }, { // 23.4.3.1 WeakSet.prototype.add(value) add: function add(value){ return weak.def(this, value, true); } }, weak, false, true); /***/ }), /* 225 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , $typed = __webpack_require__(226) , buffer = __webpack_require__(227) , anObject = __webpack_require__(19) , toIndex = __webpack_require__(46) , toLength = __webpack_require__(44) , isObject = __webpack_require__(20) , ArrayBuffer = __webpack_require__(11).ArrayBuffer , speciesConstructor = __webpack_require__(214) , $ArrayBuffer = buffer.ArrayBuffer , $DataView = buffer.DataView , $isView = $typed.ABV && ArrayBuffer.isView , $slice = $ArrayBuffer.prototype.slice , VIEW = $typed.VIEW , ARRAY_BUFFER = 'ArrayBuffer'; $export($export.G + $export.W + $export.F * (ArrayBuffer !== $ArrayBuffer), {ArrayBuffer: $ArrayBuffer}); $export($export.S + $export.F * !$typed.CONSTR, ARRAY_BUFFER, { // 24.1.3.1 ArrayBuffer.isView(arg) isView: function isView(it){ return $isView && $isView(it) || isObject(it) && VIEW in it; } }); $export($export.P + $export.U + $export.F * __webpack_require__(14)(function(){ return !new $ArrayBuffer(2).slice(1, undefined).byteLength; }), ARRAY_BUFFER, { // 24.1.4.3 ArrayBuffer.prototype.slice(start, end) slice: function slice(start, end){ if($slice !== undefined && end === undefined)return $slice.call(anObject(this), start); // FF fix var len = anObject(this).byteLength , first = toIndex(start, len) , final = toIndex(end === undefined ? len : end, len) , result = new (speciesConstructor(this, $ArrayBuffer))(toLength(final - first)) , viewS = new $DataView(this) , viewT = new $DataView(result) , index = 0; while(first < final){ viewT.setUint8(index++, viewS.getUint8(first++)); } return result; } }); __webpack_require__(199)(ARRAY_BUFFER); /***/ }), /* 226 */ /***/ (function(module, exports, __webpack_require__) { var global = __webpack_require__(11) , hide = __webpack_require__(17) , uid = __webpack_require__(26) , TYPED = uid('typed_array') , VIEW = uid('view') , ABV = !!(global.ArrayBuffer && global.DataView) , CONSTR = ABV , i = 0, l = 9, Typed; var TypedArrayConstructors = ( 'Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array' ).split(','); while(i < l){ if(Typed = global[TypedArrayConstructors[i++]]){ hide(Typed.prototype, TYPED, true); hide(Typed.prototype, VIEW, true); } else CONSTR = false; } module.exports = { ABV: ABV, CONSTR: CONSTR, TYPED: TYPED, VIEW: VIEW }; /***/ }), /* 227 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var global = __webpack_require__(11) , DESCRIPTORS = __webpack_require__(13) , LIBRARY = __webpack_require__(35) , $typed = __webpack_require__(226) , hide = __webpack_require__(17) , redefineAll = __webpack_require__(217) , fails = __webpack_require__(14) , anInstance = __webpack_require__(212) , toInteger = __webpack_require__(45) , toLength = __webpack_require__(44) , gOPN = __webpack_require__(57).f , dP = __webpack_require__(18).f , arrayFill = __webpack_require__(195) , setToStringTag = __webpack_require__(31) , ARRAY_BUFFER = 'ArrayBuffer' , DATA_VIEW = 'DataView' , PROTOTYPE = 'prototype' , WRONG_LENGTH = 'Wrong length!' , WRONG_INDEX = 'Wrong index!' , $ArrayBuffer = global[ARRAY_BUFFER] , $DataView = global[DATA_VIEW] , Math = global.Math , RangeError = global.RangeError , Infinity = global.Infinity , BaseBuffer = $ArrayBuffer , abs = Math.abs , pow = Math.pow , floor = Math.floor , log = Math.log , LN2 = Math.LN2 , BUFFER = 'buffer' , BYTE_LENGTH = 'byteLength' , BYTE_OFFSET = 'byteOffset' , $BUFFER = DESCRIPTORS ? '_b' : BUFFER , $LENGTH = DESCRIPTORS ? '_l' : BYTE_LENGTH , $OFFSET = DESCRIPTORS ? '_o' : BYTE_OFFSET; // IEEE754 conversions based on https://github.com/feross/ieee754 var packIEEE754 = function(value, mLen, nBytes){ var buffer = Array(nBytes) , eLen = nBytes * 8 - mLen - 1 , eMax = (1 << eLen) - 1 , eBias = eMax >> 1 , rt = mLen === 23 ? pow(2, -24) - pow(2, -77) : 0 , i = 0 , s = value < 0 || value === 0 && 1 / value < 0 ? 1 : 0 , e, m, c; value = abs(value) if(value != value || value === Infinity){ m = value != value ? 1 : 0; e = eMax; } else { e = floor(log(value) / LN2); if(value * (c = pow(2, -e)) < 1){ e--; c *= 2; } if(e + eBias >= 1){ value += rt / c; } else { value += rt * pow(2, 1 - eBias); } if(value * c >= 2){ e++; c /= 2; } if(e + eBias >= eMax){ m = 0; e = eMax; } else if(e + eBias >= 1){ m = (value * c - 1) * pow(2, mLen); e = e + eBias; } else { m = value * pow(2, eBias - 1) * pow(2, mLen); e = 0; } } for(; mLen >= 8; buffer[i++] = m & 255, m /= 256, mLen -= 8); e = e << mLen | m; eLen += mLen; for(; eLen > 0; buffer[i++] = e & 255, e /= 256, eLen -= 8); buffer[--i] |= s * 128; return buffer; }; var unpackIEEE754 = function(buffer, mLen, nBytes){ var eLen = nBytes * 8 - mLen - 1 , eMax = (1 << eLen) - 1 , eBias = eMax >> 1 , nBits = eLen - 7 , i = nBytes - 1 , s = buffer[i--] , e = s & 127 , m; s >>= 7; for(; nBits > 0; e = e * 256 + buffer[i], i--, nBits -= 8); m = e & (1 << -nBits) - 1; e >>= -nBits; nBits += mLen; for(; nBits > 0; m = m * 256 + buffer[i], i--, nBits -= 8); if(e === 0){ e = 1 - eBias; } else if(e === eMax){ return m ? NaN : s ? -Infinity : Infinity; } else { m = m + pow(2, mLen); e = e - eBias; } return (s ? -1 : 1) * m * pow(2, e - mLen); }; var unpackI32 = function(bytes){ return bytes[3] << 24 | bytes[2] << 16 | bytes[1] << 8 | bytes[0]; }; var packI8 = function(it){ return [it & 0xff]; }; var packI16 = function(it){ return [it & 0xff, it >> 8 & 0xff]; }; var packI32 = function(it){ return [it & 0xff, it >> 8 & 0xff, it >> 16 & 0xff, it >> 24 & 0xff]; }; var packF64 = function(it){ return packIEEE754(it, 52, 8); }; var packF32 = function(it){ return packIEEE754(it, 23, 4); }; var addGetter = function(C, key, internal){ dP(C[PROTOTYPE], key, {get: function(){ return this[internal]; }}); }; var get = function(view, bytes, index, isLittleEndian){ var numIndex = +index , intIndex = toInteger(numIndex); if(numIndex != intIndex || intIndex < 0 || intIndex + bytes > view[$LENGTH])throw RangeError(WRONG_INDEX); var store = view[$BUFFER]._b , start = intIndex + view[$OFFSET] , pack = store.slice(start, start + bytes); return isLittleEndian ? pack : pack.reverse(); }; var set = function(view, bytes, index, conversion, value, isLittleEndian){ var numIndex = +index , intIndex = toInteger(numIndex); if(numIndex != intIndex || intIndex < 0 || intIndex + bytes > view[$LENGTH])throw RangeError(WRONG_INDEX); var store = view[$BUFFER]._b , start = intIndex + view[$OFFSET] , pack = conversion(+value); for(var i = 0; i < bytes; i++)store[start + i] = pack[isLittleEndian ? i : bytes - i - 1]; }; var validateArrayBufferArguments = function(that, length){ anInstance(that, $ArrayBuffer, ARRAY_BUFFER); var numberLength = +length , byteLength = toLength(numberLength); if(numberLength != byteLength)throw RangeError(WRONG_LENGTH); return byteLength; }; if(!$typed.ABV){ $ArrayBuffer = function ArrayBuffer(length){ var byteLength = validateArrayBufferArguments(this, length); this._b = arrayFill.call(Array(byteLength), 0); this[$LENGTH] = byteLength; }; $DataView = function DataView(buffer, byteOffset, byteLength){ anInstance(this, $DataView, DATA_VIEW); anInstance(buffer, $ArrayBuffer, DATA_VIEW); var bufferLength = buffer[$LENGTH] , offset = toInteger(byteOffset); if(offset < 0 || offset > bufferLength)throw RangeError('Wrong offset!'); byteLength = byteLength === undefined ? bufferLength - offset : toLength(byteLength); if(offset + byteLength > bufferLength)throw RangeError(WRONG_LENGTH); this[$BUFFER] = buffer; this[$OFFSET] = offset; this[$LENGTH] = byteLength; }; if(DESCRIPTORS){ addGetter($ArrayBuffer, BYTE_LENGTH, '_l'); addGetter($DataView, BUFFER, '_b'); addGetter($DataView, BYTE_LENGTH, '_l'); addGetter($DataView, BYTE_OFFSET, '_o'); } redefineAll($DataView[PROTOTYPE], { getInt8: function getInt8(byteOffset){ return get(this, 1, byteOffset)[0] << 24 >> 24; }, getUint8: function getUint8(byteOffset){ return get(this, 1, byteOffset)[0]; }, getInt16: function getInt16(byteOffset /*, littleEndian */){ var bytes = get(this, 2, byteOffset, arguments[1]); return (bytes[1] << 8 | bytes[0]) << 16 >> 16; }, getUint16: function getUint16(byteOffset /*, littleEndian */){ var bytes = get(this, 2, byteOffset, arguments[1]); return bytes[1] << 8 | bytes[0]; }, getInt32: function getInt32(byteOffset /*, littleEndian */){ return unpackI32(get(this, 4, byteOffset, arguments[1])); }, getUint32: function getUint32(byteOffset /*, littleEndian */){ return unpackI32(get(this, 4, byteOffset, arguments[1])) >>> 0; }, getFloat32: function getFloat32(byteOffset /*, littleEndian */){ return unpackIEEE754(get(this, 4, byteOffset, arguments[1]), 23, 4); }, getFloat64: function getFloat64(byteOffset /*, littleEndian */){ return unpackIEEE754(get(this, 8, byteOffset, arguments[1]), 52, 8); }, setInt8: function setInt8(byteOffset, value){ set(this, 1, byteOffset, packI8, value); }, setUint8: function setUint8(byteOffset, value){ set(this, 1, byteOffset, packI8, value); }, setInt16: function setInt16(byteOffset, value /*, littleEndian */){ set(this, 2, byteOffset, packI16, value, arguments[2]); }, setUint16: function setUint16(byteOffset, value /*, littleEndian */){ set(this, 2, byteOffset, packI16, value, arguments[2]); }, setInt32: function setInt32(byteOffset, value /*, littleEndian */){ set(this, 4, byteOffset, packI32, value, arguments[2]); }, setUint32: function setUint32(byteOffset, value /*, littleEndian */){ set(this, 4, byteOffset, packI32, value, arguments[2]); }, setFloat32: function setFloat32(byteOffset, value /*, littleEndian */){ set(this, 4, byteOffset, packF32, value, arguments[2]); }, setFloat64: function setFloat64(byteOffset, value /*, littleEndian */){ set(this, 8, byteOffset, packF64, value, arguments[2]); } }); } else { if(!fails(function(){ new $ArrayBuffer; // eslint-disable-line no-new }) || !fails(function(){ new $ArrayBuffer(.5); // eslint-disable-line no-new })){ $ArrayBuffer = function ArrayBuffer(length){ return new BaseBuffer(validateArrayBufferArguments(this, length)); }; var ArrayBufferProto = $ArrayBuffer[PROTOTYPE] = BaseBuffer[PROTOTYPE]; for(var keys = gOPN(BaseBuffer), j = 0, key; keys.length > j; ){ if(!((key = keys[j++]) in $ArrayBuffer))hide($ArrayBuffer, key, BaseBuffer[key]); }; if(!LIBRARY)ArrayBufferProto.constructor = $ArrayBuffer; } // iOS Safari 7.x bug var view = new $DataView(new $ArrayBuffer(2)) , $setInt8 = $DataView[PROTOTYPE].setInt8; view.setInt8(0, 2147483648); view.setInt8(1, 2147483649); if(view.getInt8(0) || !view.getInt8(1))redefineAll($DataView[PROTOTYPE], { setInt8: function setInt8(byteOffset, value){ $setInt8.call(this, byteOffset, value << 24 >> 24); }, setUint8: function setUint8(byteOffset, value){ $setInt8.call(this, byteOffset, value << 24 >> 24); } }, true); } setToStringTag($ArrayBuffer, ARRAY_BUFFER); setToStringTag($DataView, DATA_VIEW); hide($DataView[PROTOTYPE], $typed.VIEW, true); exports[ARRAY_BUFFER] = $ArrayBuffer; exports[DATA_VIEW] = $DataView; /***/ }), /* 228 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15); $export($export.G + $export.W + $export.F * !__webpack_require__(226).ABV, { DataView: __webpack_require__(227).DataView }); /***/ }), /* 229 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Int8', 1, function(init){ return function Int8Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 230 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; if(__webpack_require__(13)){ var LIBRARY = __webpack_require__(35) , global = __webpack_require__(11) , fails = __webpack_require__(14) , $export = __webpack_require__(15) , $typed = __webpack_require__(226) , $buffer = __webpack_require__(227) , ctx = __webpack_require__(27) , anInstance = __webpack_require__(212) , propertyDesc = __webpack_require__(24) , hide = __webpack_require__(17) , redefineAll = __webpack_require__(217) , toInteger = __webpack_require__(45) , toLength = __webpack_require__(44) , toIndex = __webpack_require__(46) , toPrimitive = __webpack_require__(23) , has = __webpack_require__(12) , same = __webpack_require__(78) , classof = __webpack_require__(82) , isObject = __webpack_require__(20) , toObject = __webpack_require__(65) , isArrayIter = __webpack_require__(169) , create = __webpack_require__(53) , getPrototypeOf = __webpack_require__(66) , gOPN = __webpack_require__(57).f , getIterFn = __webpack_require__(171) , uid = __webpack_require__(26) , wks = __webpack_require__(32) , createArrayMethod = __webpack_require__(179) , createArrayIncludes = __webpack_require__(43) , speciesConstructor = __webpack_require__(214) , ArrayIterators = __webpack_require__(200) , Iterators = __webpack_require__(136) , $iterDetect = __webpack_require__(172) , setSpecies = __webpack_require__(199) , arrayFill = __webpack_require__(195) , arrayCopyWithin = __webpack_require__(192) , $DP = __webpack_require__(18) , $GOPD = __webpack_require__(58) , dP = $DP.f , gOPD = $GOPD.f , RangeError = global.RangeError , TypeError = global.TypeError , Uint8Array = global.Uint8Array , ARRAY_BUFFER = 'ArrayBuffer' , SHARED_BUFFER = 'Shared' + ARRAY_BUFFER , BYTES_PER_ELEMENT = 'BYTES_PER_ELEMENT' , PROTOTYPE = 'prototype' , ArrayProto = Array[PROTOTYPE] , $ArrayBuffer = $buffer.ArrayBuffer , $DataView = $buffer.DataView , arrayForEach = createArrayMethod(0) , arrayFilter = createArrayMethod(2) , arraySome = createArrayMethod(3) , arrayEvery = createArrayMethod(4) , arrayFind = createArrayMethod(5) , arrayFindIndex = createArrayMethod(6) , arrayIncludes = createArrayIncludes(true) , arrayIndexOf = createArrayIncludes(false) , arrayValues = ArrayIterators.values , arrayKeys = ArrayIterators.keys , arrayEntries = ArrayIterators.entries , arrayLastIndexOf = ArrayProto.lastIndexOf , arrayReduce = ArrayProto.reduce , arrayReduceRight = ArrayProto.reduceRight , arrayJoin = ArrayProto.join , arraySort = ArrayProto.sort , arraySlice = ArrayProto.slice , arrayToString = ArrayProto.toString , arrayToLocaleString = ArrayProto.toLocaleString , ITERATOR = wks('iterator') , TAG = wks('toStringTag') , TYPED_CONSTRUCTOR = uid('typed_constructor') , DEF_CONSTRUCTOR = uid('def_constructor') , ALL_CONSTRUCTORS = $typed.CONSTR , TYPED_ARRAY = $typed.TYPED , VIEW = $typed.VIEW , WRONG_LENGTH = 'Wrong length!'; var $map = createArrayMethod(1, function(O, length){ return allocate(speciesConstructor(O, O[DEF_CONSTRUCTOR]), length); }); var LITTLE_ENDIAN = fails(function(){ return new Uint8Array(new Uint16Array([1]).buffer)[0] === 1; }); var FORCED_SET = !!Uint8Array && !!Uint8Array[PROTOTYPE].set && fails(function(){ new Uint8Array(1).set({}); }); var strictToLength = function(it, SAME){ if(it === undefined)throw TypeError(WRONG_LENGTH); var number = +it , length = toLength(it); if(SAME && !same(number, length))throw RangeError(WRONG_LENGTH); return length; }; var toOffset = function(it, BYTES){ var offset = toInteger(it); if(offset < 0 || offset % BYTES)throw RangeError('Wrong offset!'); return offset; }; var validate = function(it){ if(isObject(it) && TYPED_ARRAY in it)return it; throw TypeError(it + ' is not a typed array!'); }; var allocate = function(C, length){ if(!(isObject(C) && TYPED_CONSTRUCTOR in C)){ throw TypeError('It is not a typed array constructor!'); } return new C(length); }; var speciesFromList = function(O, list){ return fromList(speciesConstructor(O, O[DEF_CONSTRUCTOR]), list); }; var fromList = function(C, list){ var index = 0 , length = list.length , result = allocate(C, length); while(length > index)result[index] = list[index++]; return result; }; var addGetter = function(it, key, internal){ dP(it, key, {get: function(){ return this._d[internal]; }}); }; var $from = function from(source /*, mapfn, thisArg */){ var O = toObject(source) , aLen = arguments.length , mapfn = aLen > 1 ? arguments[1] : undefined , mapping = mapfn !== undefined , iterFn = getIterFn(O) , i, length, values, result, step, iterator; if(iterFn != undefined && !isArrayIter(iterFn)){ for(iterator = iterFn.call(O), values = [], i = 0; !(step = iterator.next()).done; i++){ values.push(step.value); } O = values; } if(mapping && aLen > 2)mapfn = ctx(mapfn, arguments[2], 2); for(i = 0, length = toLength(O.length), result = allocate(this, length); length > i; i++){ result[i] = mapping ? mapfn(O[i], i) : O[i]; } return result; }; var $of = function of(/*...items*/){ var index = 0 , length = arguments.length , result = allocate(this, length); while(length > index)result[index] = arguments[index++]; return result; }; // iOS Safari 6.x fails here var TO_LOCALE_BUG = !!Uint8Array && fails(function(){ arrayToLocaleString.call(new Uint8Array(1)); }); var $toLocaleString = function toLocaleString(){ return arrayToLocaleString.apply(TO_LOCALE_BUG ? arraySlice.call(validate(this)) : validate(this), arguments); }; var proto = { copyWithin: function copyWithin(target, start /*, end */){ return arrayCopyWithin.call(validate(this), target, start, arguments.length > 2 ? arguments[2] : undefined); }, every: function every(callbackfn /*, thisArg */){ return arrayEvery(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined); }, fill: function fill(value /*, start, end */){ // eslint-disable-line no-unused-vars return arrayFill.apply(validate(this), arguments); }, filter: function filter(callbackfn /*, thisArg */){ return speciesFromList(this, arrayFilter(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined)); }, find: function find(predicate /*, thisArg */){ return arrayFind(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined); }, findIndex: function findIndex(predicate /*, thisArg */){ return arrayFindIndex(validate(this), predicate, arguments.length > 1 ? arguments[1] : undefined); }, forEach: function forEach(callbackfn /*, thisArg */){ arrayForEach(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined); }, indexOf: function indexOf(searchElement /*, fromIndex */){ return arrayIndexOf(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined); }, includes: function includes(searchElement /*, fromIndex */){ return arrayIncludes(validate(this), searchElement, arguments.length > 1 ? arguments[1] : undefined); }, join: function join(separator){ // eslint-disable-line no-unused-vars return arrayJoin.apply(validate(this), arguments); }, lastIndexOf: function lastIndexOf(searchElement /*, fromIndex */){ // eslint-disable-line no-unused-vars return arrayLastIndexOf.apply(validate(this), arguments); }, map: function map(mapfn /*, thisArg */){ return $map(validate(this), mapfn, arguments.length > 1 ? arguments[1] : undefined); }, reduce: function reduce(callbackfn /*, initialValue */){ // eslint-disable-line no-unused-vars return arrayReduce.apply(validate(this), arguments); }, reduceRight: function reduceRight(callbackfn /*, initialValue */){ // eslint-disable-line no-unused-vars return arrayReduceRight.apply(validate(this), arguments); }, reverse: function reverse(){ var that = this , length = validate(that).length , middle = Math.floor(length / 2) , index = 0 , value; while(index < middle){ value = that[index]; that[index++] = that[--length]; that[length] = value; } return that; }, some: function some(callbackfn /*, thisArg */){ return arraySome(validate(this), callbackfn, arguments.length > 1 ? arguments[1] : undefined); }, sort: function sort(comparefn){ return arraySort.call(validate(this), comparefn); }, subarray: function subarray(begin, end){ var O = validate(this) , length = O.length , $begin = toIndex(begin, length); return new (speciesConstructor(O, O[DEF_CONSTRUCTOR]))( O.buffer, O.byteOffset + $begin * O.BYTES_PER_ELEMENT, toLength((end === undefined ? length : toIndex(end, length)) - $begin) ); } }; var $slice = function slice(start, end){ return speciesFromList(this, arraySlice.call(validate(this), start, end)); }; var $set = function set(arrayLike /*, offset */){ validate(this); var offset = toOffset(arguments[1], 1) , length = this.length , src = toObject(arrayLike) , len = toLength(src.length) , index = 0; if(len + offset > length)throw RangeError(WRONG_LENGTH); while(index < len)this[offset + index] = src[index++]; }; var $iterators = { entries: function entries(){ return arrayEntries.call(validate(this)); }, keys: function keys(){ return arrayKeys.call(validate(this)); }, values: function values(){ return arrayValues.call(validate(this)); } }; var isTAIndex = function(target, key){ return isObject(target) && target[TYPED_ARRAY] && typeof key != 'symbol' && key in target && String(+key) == String(key); }; var $getDesc = function getOwnPropertyDescriptor(target, key){ return isTAIndex(target, key = toPrimitive(key, true)) ? propertyDesc(2, target[key]) : gOPD(target, key); }; var $setDesc = function defineProperty(target, key, desc){ if(isTAIndex(target, key = toPrimitive(key, true)) && isObject(desc) && has(desc, 'value') && !has(desc, 'get') && !has(desc, 'set') // TODO: add validation descriptor w/o calling accessors && !desc.configurable && (!has(desc, 'writable') || desc.writable) && (!has(desc, 'enumerable') || desc.enumerable) ){ target[key] = desc.value; return target; } else return dP(target, key, desc); }; if(!ALL_CONSTRUCTORS){ $GOPD.f = $getDesc; $DP.f = $setDesc; } $export($export.S + $export.F * !ALL_CONSTRUCTORS, 'Object', { getOwnPropertyDescriptor: $getDesc, defineProperty: $setDesc }); if(fails(function(){ arrayToString.call({}); })){ arrayToString = arrayToLocaleString = function toString(){ return arrayJoin.call(this); } } var $TypedArrayPrototype$ = redefineAll({}, proto); redefineAll($TypedArrayPrototype$, $iterators); hide($TypedArrayPrototype$, ITERATOR, $iterators.values); redefineAll($TypedArrayPrototype$, { slice: $slice, set: $set, constructor: function(){ /* noop */ }, toString: arrayToString, toLocaleString: $toLocaleString }); addGetter($TypedArrayPrototype$, 'buffer', 'b'); addGetter($TypedArrayPrototype$, 'byteOffset', 'o'); addGetter($TypedArrayPrototype$, 'byteLength', 'l'); addGetter($TypedArrayPrototype$, 'length', 'e'); dP($TypedArrayPrototype$, TAG, { get: function(){ return this[TYPED_ARRAY]; } }); module.exports = function(KEY, BYTES, wrapper, CLAMPED){ CLAMPED = !!CLAMPED; var NAME = KEY + (CLAMPED ? 'Clamped' : '') + 'Array' , ISNT_UINT8 = NAME != 'Uint8Array' , GETTER = 'get' + KEY , SETTER = 'set' + KEY , TypedArray = global[NAME] , Base = TypedArray || {} , TAC = TypedArray && getPrototypeOf(TypedArray) , FORCED = !TypedArray || !$typed.ABV , O = {} , TypedArrayPrototype = TypedArray && TypedArray[PROTOTYPE]; var getter = function(that, index){ var data = that._d; return data.v[GETTER](index * BYTES + data.o, LITTLE_ENDIAN); }; var setter = function(that, index, value){ var data = that._d; if(CLAMPED)value = (value = Math.round(value)) < 0 ? 0 : value > 0xff ? 0xff : value & 0xff; data.v[SETTER](index * BYTES + data.o, value, LITTLE_ENDIAN); }; var addElement = function(that, index){ dP(that, index, { get: function(){ return getter(this, index); }, set: function(value){ return setter(this, index, value); }, enumerable: true }); }; if(FORCED){ TypedArray = wrapper(function(that, data, $offset, $length){ anInstance(that, TypedArray, NAME, '_d'); var index = 0 , offset = 0 , buffer, byteLength, length, klass; if(!isObject(data)){ length = strictToLength(data, true) byteLength = length * BYTES; buffer = new $ArrayBuffer(byteLength); } else if(data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER){ buffer = data; offset = toOffset($offset, BYTES); var $len = data.byteLength; if($length === undefined){ if($len % BYTES)throw RangeError(WRONG_LENGTH); byteLength = $len - offset; if(byteLength < 0)throw RangeError(WRONG_LENGTH); } else { byteLength = toLength($length) * BYTES; if(byteLength + offset > $len)throw RangeError(WRONG_LENGTH); } length = byteLength / BYTES; } else if(TYPED_ARRAY in data){ return fromList(TypedArray, data); } else { return $from.call(TypedArray, data); } hide(that, '_d', { b: buffer, o: offset, l: byteLength, e: length, v: new $DataView(buffer) }); while(index < length)addElement(that, index++); }); TypedArrayPrototype = TypedArray[PROTOTYPE] = create($TypedArrayPrototype$); hide(TypedArrayPrototype, 'constructor', TypedArray); } else if(!$iterDetect(function(iter){ // V8 works with iterators, but fails in many other cases // https://code.google.com/p/v8/issues/detail?id=4552 new TypedArray(null); // eslint-disable-line no-new new TypedArray(iter); // eslint-disable-line no-new }, true)){ TypedArray = wrapper(function(that, data, $offset, $length){ anInstance(that, TypedArray, NAME); var klass; // `ws` module bug, temporarily remove validation length for Uint8Array // https://github.com/websockets/ws/pull/645 if(!isObject(data))return new Base(strictToLength(data, ISNT_UINT8)); if(data instanceof $ArrayBuffer || (klass = classof(data)) == ARRAY_BUFFER || klass == SHARED_BUFFER){ return $length !== undefined ? new Base(data, toOffset($offset, BYTES), $length) : $offset !== undefined ? new Base(data, toOffset($offset, BYTES)) : new Base(data); } if(TYPED_ARRAY in data)return fromList(TypedArray, data); return $from.call(TypedArray, data); }); arrayForEach(TAC !== Function.prototype ? gOPN(Base).concat(gOPN(TAC)) : gOPN(Base), function(key){ if(!(key in TypedArray))hide(TypedArray, key, Base[key]); }); TypedArray[PROTOTYPE] = TypedArrayPrototype; if(!LIBRARY)TypedArrayPrototype.constructor = TypedArray; } var $nativeIterator = TypedArrayPrototype[ITERATOR] , CORRECT_ITER_NAME = !!$nativeIterator && ($nativeIterator.name == 'values' || $nativeIterator.name == undefined) , $iterator = $iterators.values; hide(TypedArray, TYPED_CONSTRUCTOR, true); hide(TypedArrayPrototype, TYPED_ARRAY, NAME); hide(TypedArrayPrototype, VIEW, true); hide(TypedArrayPrototype, DEF_CONSTRUCTOR, TypedArray); if(CLAMPED ? new TypedArray(1)[TAG] != NAME : !(TAG in TypedArrayPrototype)){ dP(TypedArrayPrototype, TAG, { get: function(){ return NAME; } }); } O[NAME] = TypedArray; $export($export.G + $export.W + $export.F * (TypedArray != Base), O); $export($export.S, NAME, { BYTES_PER_ELEMENT: BYTES, from: $from, of: $of }); if(!(BYTES_PER_ELEMENT in TypedArrayPrototype))hide(TypedArrayPrototype, BYTES_PER_ELEMENT, BYTES); $export($export.P, NAME, proto); setSpecies(NAME); $export($export.P + $export.F * FORCED_SET, NAME, {set: $set}); $export($export.P + $export.F * !CORRECT_ITER_NAME, NAME, $iterators); $export($export.P + $export.F * (TypedArrayPrototype.toString != arrayToString), NAME, {toString: arrayToString}); $export($export.P + $export.F * fails(function(){ new TypedArray(1).slice(); }), NAME, {slice: $slice}); $export($export.P + $export.F * (fails(function(){ return [1, 2].toLocaleString() != new TypedArray([1, 2]).toLocaleString() }) || !fails(function(){ TypedArrayPrototype.toLocaleString.call([1, 2]); })), NAME, {toLocaleString: $toLocaleString}); Iterators[NAME] = CORRECT_ITER_NAME ? $nativeIterator : $iterator; if(!LIBRARY && !CORRECT_ITER_NAME)hide(TypedArrayPrototype, ITERATOR, $iterator); }; } else module.exports = function(){ /* empty */ }; /***/ }), /* 231 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Uint8', 1, function(init){ return function Uint8Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 232 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Uint8', 1, function(init){ return function Uint8ClampedArray(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }, true); /***/ }), /* 233 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Int16', 2, function(init){ return function Int16Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 234 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Uint16', 2, function(init){ return function Uint16Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 235 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Int32', 4, function(init){ return function Int32Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 236 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Uint32', 4, function(init){ return function Uint32Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 237 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Float32', 4, function(init){ return function Float32Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 238 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(230)('Float64', 8, function(init){ return function Float64Array(data, byteOffset, length){ return init(this, data, byteOffset, length); }; }); /***/ }), /* 239 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.1 Reflect.apply(target, thisArgument, argumentsList) var $export = __webpack_require__(15) , aFunction = __webpack_require__(28) , anObject = __webpack_require__(19) , rApply = (__webpack_require__(11).Reflect || {}).apply , fApply = Function.apply; // MS Edge argumentsList argument is optional $export($export.S + $export.F * !__webpack_require__(14)(function(){ rApply(function(){}); }), 'Reflect', { apply: function apply(target, thisArgument, argumentsList){ var T = aFunction(target) , L = anObject(argumentsList); return rApply ? rApply(T, thisArgument, L) : fApply.call(T, thisArgument, L); } }); /***/ }), /* 240 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.2 Reflect.construct(target, argumentsList [, newTarget]) var $export = __webpack_require__(15) , create = __webpack_require__(53) , aFunction = __webpack_require__(28) , anObject = __webpack_require__(19) , isObject = __webpack_require__(20) , fails = __webpack_require__(14) , bind = __webpack_require__(84) , rConstruct = (__webpack_require__(11).Reflect || {}).construct; // MS Edge supports only 2 arguments and argumentsList argument is optional // FF Nightly sets third argument as `new.target`, but does not create `this` from it var NEW_TARGET_BUG = fails(function(){ function F(){} return !(rConstruct(function(){}, [], F) instanceof F); }); var ARGS_BUG = !fails(function(){ rConstruct(function(){}); }); $export($export.S + $export.F * (NEW_TARGET_BUG || ARGS_BUG), 'Reflect', { construct: function construct(Target, args /*, newTarget*/){ aFunction(Target); anObject(args); var newTarget = arguments.length < 3 ? Target : aFunction(arguments[2]); if(ARGS_BUG && !NEW_TARGET_BUG)return rConstruct(Target, args, newTarget); if(Target == newTarget){ // w/o altered newTarget, optimization for 0-4 arguments switch(args.length){ case 0: return new Target; case 1: return new Target(args[0]); case 2: return new Target(args[0], args[1]); case 3: return new Target(args[0], args[1], args[2]); case 4: return new Target(args[0], args[1], args[2], args[3]); } // w/o altered newTarget, lot of arguments case var $args = [null]; $args.push.apply($args, args); return new (bind.apply(Target, $args)); } // with altered newTarget, not support built-in constructors var proto = newTarget.prototype , instance = create(isObject(proto) ? proto : Object.prototype) , result = Function.apply.call(Target, instance, args); return isObject(result) ? result : instance; } }); /***/ }), /* 241 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.3 Reflect.defineProperty(target, propertyKey, attributes) var dP = __webpack_require__(18) , $export = __webpack_require__(15) , anObject = __webpack_require__(19) , toPrimitive = __webpack_require__(23); // MS Edge has broken Reflect.defineProperty - throwing instead of returning false $export($export.S + $export.F * __webpack_require__(14)(function(){ Reflect.defineProperty(dP.f({}, 1, {value: 1}), 1, {value: 2}); }), 'Reflect', { defineProperty: function defineProperty(target, propertyKey, attributes){ anObject(target); propertyKey = toPrimitive(propertyKey, true); anObject(attributes); try { dP.f(target, propertyKey, attributes); return true; } catch(e){ return false; } } }); /***/ }), /* 242 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.4 Reflect.deleteProperty(target, propertyKey) var $export = __webpack_require__(15) , gOPD = __webpack_require__(58).f , anObject = __webpack_require__(19); $export($export.S, 'Reflect', { deleteProperty: function deleteProperty(target, propertyKey){ var desc = gOPD(anObject(target), propertyKey); return desc && !desc.configurable ? false : delete target[propertyKey]; } }); /***/ }), /* 243 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // 26.1.5 Reflect.enumerate(target) var $export = __webpack_require__(15) , anObject = __webpack_require__(19); var Enumerate = function(iterated){ this._t = anObject(iterated); // target this._i = 0; // next index var keys = this._k = [] // keys , key; for(key in iterated)keys.push(key); }; __webpack_require__(137)(Enumerate, 'Object', function(){ var that = this , keys = that._k , key; do { if(that._i >= keys.length)return {value: undefined, done: true}; } while(!((key = keys[that._i++]) in that._t)); return {value: key, done: false}; }); $export($export.S, 'Reflect', { enumerate: function enumerate(target){ return new Enumerate(target); } }); /***/ }), /* 244 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.6 Reflect.get(target, propertyKey [, receiver]) var gOPD = __webpack_require__(58) , getPrototypeOf = __webpack_require__(66) , has = __webpack_require__(12) , $export = __webpack_require__(15) , isObject = __webpack_require__(20) , anObject = __webpack_require__(19); function get(target, propertyKey/*, receiver*/){ var receiver = arguments.length < 3 ? target : arguments[2] , desc, proto; if(anObject(target) === receiver)return target[propertyKey]; if(desc = gOPD.f(target, propertyKey))return has(desc, 'value') ? desc.value : desc.get !== undefined ? desc.get.call(receiver) : undefined; if(isObject(proto = getPrototypeOf(target)))return get(proto, propertyKey, receiver); } $export($export.S, 'Reflect', {get: get}); /***/ }), /* 245 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.7 Reflect.getOwnPropertyDescriptor(target, propertyKey) var gOPD = __webpack_require__(58) , $export = __webpack_require__(15) , anObject = __webpack_require__(19); $export($export.S, 'Reflect', { getOwnPropertyDescriptor: function getOwnPropertyDescriptor(target, propertyKey){ return gOPD.f(anObject(target), propertyKey); } }); /***/ }), /* 246 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.8 Reflect.getPrototypeOf(target) var $export = __webpack_require__(15) , getProto = __webpack_require__(66) , anObject = __webpack_require__(19); $export($export.S, 'Reflect', { getPrototypeOf: function getPrototypeOf(target){ return getProto(anObject(target)); } }); /***/ }), /* 247 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.9 Reflect.has(target, propertyKey) var $export = __webpack_require__(15); $export($export.S, 'Reflect', { has: function has(target, propertyKey){ return propertyKey in target; } }); /***/ }), /* 248 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.10 Reflect.isExtensible(target) var $export = __webpack_require__(15) , anObject = __webpack_require__(19) , $isExtensible = Object.isExtensible; $export($export.S, 'Reflect', { isExtensible: function isExtensible(target){ anObject(target); return $isExtensible ? $isExtensible(target) : true; } }); /***/ }), /* 249 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.11 Reflect.ownKeys(target) var $export = __webpack_require__(15); $export($export.S, 'Reflect', {ownKeys: __webpack_require__(250)}); /***/ }), /* 250 */ /***/ (function(module, exports, __webpack_require__) { // all object keys, includes non-enumerable and symbols var gOPN = __webpack_require__(57) , gOPS = __webpack_require__(50) , anObject = __webpack_require__(19) , Reflect = __webpack_require__(11).Reflect; module.exports = Reflect && Reflect.ownKeys || function ownKeys(it){ var keys = gOPN.f(anObject(it)) , getSymbols = gOPS.f; return getSymbols ? keys.concat(getSymbols(it)) : keys; }; /***/ }), /* 251 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.12 Reflect.preventExtensions(target) var $export = __webpack_require__(15) , anObject = __webpack_require__(19) , $preventExtensions = Object.preventExtensions; $export($export.S, 'Reflect', { preventExtensions: function preventExtensions(target){ anObject(target); try { if($preventExtensions)$preventExtensions(target); return true; } catch(e){ return false; } } }); /***/ }), /* 252 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.13 Reflect.set(target, propertyKey, V [, receiver]) var dP = __webpack_require__(18) , gOPD = __webpack_require__(58) , getPrototypeOf = __webpack_require__(66) , has = __webpack_require__(12) , $export = __webpack_require__(15) , createDesc = __webpack_require__(24) , anObject = __webpack_require__(19) , isObject = __webpack_require__(20); function set(target, propertyKey, V/*, receiver*/){ var receiver = arguments.length < 4 ? target : arguments[3] , ownDesc = gOPD.f(anObject(target), propertyKey) , existingDescriptor, proto; if(!ownDesc){ if(isObject(proto = getPrototypeOf(target))){ return set(proto, propertyKey, V, receiver); } ownDesc = createDesc(0); } if(has(ownDesc, 'value')){ if(ownDesc.writable === false || !isObject(receiver))return false; existingDescriptor = gOPD.f(receiver, propertyKey) || createDesc(0); existingDescriptor.value = V; dP.f(receiver, propertyKey, existingDescriptor); return true; } return ownDesc.set === undefined ? false : (ownDesc.set.call(receiver, V), true); } $export($export.S, 'Reflect', {set: set}); /***/ }), /* 253 */ /***/ (function(module, exports, __webpack_require__) { // 26.1.14 Reflect.setPrototypeOf(target, proto) var $export = __webpack_require__(15) , setProto = __webpack_require__(80); if(setProto)$export($export.S, 'Reflect', { setPrototypeOf: function setPrototypeOf(target, proto){ setProto.check(target, proto); try { setProto.set(target, proto); return true; } catch(e){ return false; } } }); /***/ }), /* 254 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/tc39/Array.prototype.includes var $export = __webpack_require__(15) , $includes = __webpack_require__(43)(true); $export($export.P, 'Array', { includes: function includes(el /*, fromIndex = 0 */){ return $includes(this, el, arguments.length > 1 ? arguments[1] : undefined); } }); __webpack_require__(193)('includes'); /***/ }), /* 255 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/mathiasbynens/String.prototype.at var $export = __webpack_require__(15) , $at = __webpack_require__(134)(true); $export($export.P, 'String', { at: function at(pos){ return $at(this, pos); } }); /***/ }), /* 256 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/tc39/proposal-string-pad-start-end var $export = __webpack_require__(15) , $pad = __webpack_require__(257); $export($export.P, 'String', { padStart: function padStart(maxLength /*, fillString = ' ' */){ return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, true); } }); /***/ }), /* 257 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/tc39/proposal-string-pad-start-end var toLength = __webpack_require__(44) , repeat = __webpack_require__(98) , defined = __webpack_require__(42); module.exports = function(that, maxLength, fillString, left){ var S = String(defined(that)) , stringLength = S.length , fillStr = fillString === undefined ? ' ' : String(fillString) , intMaxLength = toLength(maxLength); if(intMaxLength <= stringLength || fillStr == '')return S; var fillLen = intMaxLength - stringLength , stringFiller = repeat.call(fillStr, Math.ceil(fillLen / fillStr.length)); if(stringFiller.length > fillLen)stringFiller = stringFiller.slice(0, fillLen); return left ? stringFiller + S : S + stringFiller; }; /***/ }), /* 258 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/tc39/proposal-string-pad-start-end var $export = __webpack_require__(15) , $pad = __webpack_require__(257); $export($export.P, 'String', { padEnd: function padEnd(maxLength /*, fillString = ' ' */){ return $pad(this, maxLength, arguments.length > 1 ? arguments[1] : undefined, false); } }); /***/ }), /* 259 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/sebmarkbage/ecmascript-string-left-right-trim __webpack_require__(90)('trimLeft', function($trim){ return function trimLeft(){ return $trim(this, 1); }; }, 'trimStart'); /***/ }), /* 260 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/sebmarkbage/ecmascript-string-left-right-trim __webpack_require__(90)('trimRight', function($trim){ return function trimRight(){ return $trim(this, 2); }; }, 'trimEnd'); /***/ }), /* 261 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://tc39.github.io/String.prototype.matchAll/ var $export = __webpack_require__(15) , defined = __webpack_require__(42) , toLength = __webpack_require__(44) , isRegExp = __webpack_require__(141) , getFlags = __webpack_require__(203) , RegExpProto = RegExp.prototype; var $RegExpStringIterator = function(regexp, string){ this._r = regexp; this._s = string; }; __webpack_require__(137)($RegExpStringIterator, 'RegExp String', function next(){ var match = this._r.exec(this._s); return {value: match, done: match === null}; }); $export($export.P, 'String', { matchAll: function matchAll(regexp){ defined(this); if(!isRegExp(regexp))throw TypeError(regexp + ' is not a regexp!'); var S = String(this) , flags = 'flags' in RegExpProto ? String(regexp.flags) : getFlags.call(regexp) , rx = new RegExp(regexp.source, ~flags.indexOf('g') ? flags : 'g' + flags); rx.lastIndex = toLength(regexp.lastIndex); return new $RegExpStringIterator(rx, S); } }); /***/ }), /* 262 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(34)('asyncIterator'); /***/ }), /* 263 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(34)('observable'); /***/ }), /* 264 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/tc39/proposal-object-getownpropertydescriptors var $export = __webpack_require__(15) , ownKeys = __webpack_require__(250) , toIObject = __webpack_require__(39) , gOPD = __webpack_require__(58) , createProperty = __webpack_require__(170); $export($export.S, 'Object', { getOwnPropertyDescriptors: function getOwnPropertyDescriptors(object){ var O = toIObject(object) , getDesc = gOPD.f , keys = ownKeys(O) , result = {} , i = 0 , key; while(keys.length > i)createProperty(result, key = keys[i++], getDesc(O, key)); return result; } }); /***/ }), /* 265 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/tc39/proposal-object-values-entries var $export = __webpack_require__(15) , $values = __webpack_require__(266)(false); $export($export.S, 'Object', { values: function values(it){ return $values(it); } }); /***/ }), /* 266 */ /***/ (function(module, exports, __webpack_require__) { var getKeys = __webpack_require__(37) , toIObject = __webpack_require__(39) , isEnum = __webpack_require__(51).f; module.exports = function(isEntries){ return function(it){ var O = toIObject(it) , keys = getKeys(O) , length = keys.length , i = 0 , result = [] , key; while(length > i)if(isEnum.call(O, key = keys[i++])){ result.push(isEntries ? [key, O[key]] : O[key]); } return result; }; }; /***/ }), /* 267 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/tc39/proposal-object-values-entries var $export = __webpack_require__(15) , $entries = __webpack_require__(266)(true); $export($export.S, 'Object', { entries: function entries(it){ return $entries(it); } }); /***/ }), /* 268 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toObject = __webpack_require__(65) , aFunction = __webpack_require__(28) , $defineProperty = __webpack_require__(18); // B.2.2.2 Object.prototype.__defineGetter__(P, getter) __webpack_require__(13) && $export($export.P + __webpack_require__(269), 'Object', { __defineGetter__: function __defineGetter__(P, getter){ $defineProperty.f(toObject(this), P, {get: aFunction(getter), enumerable: true, configurable: true}); } }); /***/ }), /* 269 */ /***/ (function(module, exports, __webpack_require__) { // Forced replacement prototype accessors methods module.exports = __webpack_require__(35)|| !__webpack_require__(14)(function(){ var K = Math.random(); // In FF throws only define methods __defineSetter__.call(null, K, function(){ /* empty */}); delete __webpack_require__(11)[K]; }); /***/ }), /* 270 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toObject = __webpack_require__(65) , aFunction = __webpack_require__(28) , $defineProperty = __webpack_require__(18); // B.2.2.3 Object.prototype.__defineSetter__(P, setter) __webpack_require__(13) && $export($export.P + __webpack_require__(269), 'Object', { __defineSetter__: function __defineSetter__(P, setter){ $defineProperty.f(toObject(this), P, {set: aFunction(setter), enumerable: true, configurable: true}); } }); /***/ }), /* 271 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toObject = __webpack_require__(65) , toPrimitive = __webpack_require__(23) , getPrototypeOf = __webpack_require__(66) , getOwnPropertyDescriptor = __webpack_require__(58).f; // B.2.2.4 Object.prototype.__lookupGetter__(P) __webpack_require__(13) && $export($export.P + __webpack_require__(269), 'Object', { __lookupGetter__: function __lookupGetter__(P){ var O = toObject(this) , K = toPrimitive(P, true) , D; do { if(D = getOwnPropertyDescriptor(O, K))return D.get; } while(O = getPrototypeOf(O)); } }); /***/ }), /* 272 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var $export = __webpack_require__(15) , toObject = __webpack_require__(65) , toPrimitive = __webpack_require__(23) , getPrototypeOf = __webpack_require__(66) , getOwnPropertyDescriptor = __webpack_require__(58).f; // B.2.2.5 Object.prototype.__lookupSetter__(P) __webpack_require__(13) && $export($export.P + __webpack_require__(269), 'Object', { __lookupSetter__: function __lookupSetter__(P){ var O = toObject(this) , K = toPrimitive(P, true) , D; do { if(D = getOwnPropertyDescriptor(O, K))return D.set; } while(O = getPrototypeOf(O)); } }); /***/ }), /* 273 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/DavidBruant/Map-Set.prototype.toJSON var $export = __webpack_require__(15); $export($export.P + $export.R, 'Map', {toJSON: __webpack_require__(274)('Map')}); /***/ }), /* 274 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/DavidBruant/Map-Set.prototype.toJSON var classof = __webpack_require__(82) , from = __webpack_require__(275); module.exports = function(NAME){ return function toJSON(){ if(classof(this) != NAME)throw TypeError(NAME + "#toJSON isn't generic"); return from(this); }; }; /***/ }), /* 275 */ /***/ (function(module, exports, __webpack_require__) { var forOf = __webpack_require__(213); module.exports = function(iter, ITERATOR){ var result = []; forOf(iter, false, result.push, result, ITERATOR); return result; }; /***/ }), /* 276 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/DavidBruant/Map-Set.prototype.toJSON var $export = __webpack_require__(15); $export($export.P + $export.R, 'Set', {toJSON: __webpack_require__(274)('Set')}); /***/ }), /* 277 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/ljharb/proposal-global var $export = __webpack_require__(15); $export($export.S, 'System', {global: __webpack_require__(11)}); /***/ }), /* 278 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/ljharb/proposal-is-error var $export = __webpack_require__(15) , cof = __webpack_require__(41); $export($export.S, 'Error', { isError: function isError(it){ return cof(it) === 'Error'; } }); /***/ }), /* 279 */ /***/ (function(module, exports, __webpack_require__) { // https://gist.github.com/BrendanEich/4294d5c212a6d2254703 var $export = __webpack_require__(15); $export($export.S, 'Math', { iaddh: function iaddh(x0, x1, y0, y1){ var $x0 = x0 >>> 0 , $x1 = x1 >>> 0 , $y0 = y0 >>> 0; return $x1 + (y1 >>> 0) + (($x0 & $y0 | ($x0 | $y0) & ~($x0 + $y0 >>> 0)) >>> 31) | 0; } }); /***/ }), /* 280 */ /***/ (function(module, exports, __webpack_require__) { // https://gist.github.com/BrendanEich/4294d5c212a6d2254703 var $export = __webpack_require__(15); $export($export.S, 'Math', { isubh: function isubh(x0, x1, y0, y1){ var $x0 = x0 >>> 0 , $x1 = x1 >>> 0 , $y0 = y0 >>> 0; return $x1 - (y1 >>> 0) - ((~$x0 & $y0 | ~($x0 ^ $y0) & $x0 - $y0 >>> 0) >>> 31) | 0; } }); /***/ }), /* 281 */ /***/ (function(module, exports, __webpack_require__) { // https://gist.github.com/BrendanEich/4294d5c212a6d2254703 var $export = __webpack_require__(15); $export($export.S, 'Math', { imulh: function imulh(u, v){ var UINT16 = 0xffff , $u = +u , $v = +v , u0 = $u & UINT16 , v0 = $v & UINT16 , u1 = $u >> 16 , v1 = $v >> 16 , t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16); return u1 * v1 + (t >> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >> 16); } }); /***/ }), /* 282 */ /***/ (function(module, exports, __webpack_require__) { // https://gist.github.com/BrendanEich/4294d5c212a6d2254703 var $export = __webpack_require__(15); $export($export.S, 'Math', { umulh: function umulh(u, v){ var UINT16 = 0xffff , $u = +u , $v = +v , u0 = $u & UINT16 , v0 = $v & UINT16 , u1 = $u >>> 16 , v1 = $v >>> 16 , t = (u1 * v0 >>> 0) + (u0 * v0 >>> 16); return u1 * v1 + (t >>> 16) + ((u0 * v1 >>> 0) + (t & UINT16) >>> 16); } }); /***/ }), /* 283 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , toMetaKey = metadata.key , ordinaryDefineOwnMetadata = metadata.set; metadata.exp({defineMetadata: function defineMetadata(metadataKey, metadataValue, target, targetKey){ ordinaryDefineOwnMetadata(metadataKey, metadataValue, anObject(target), toMetaKey(targetKey)); }}); /***/ }), /* 284 */ /***/ (function(module, exports, __webpack_require__) { var Map = __webpack_require__(218) , $export = __webpack_require__(15) , shared = __webpack_require__(30)('metadata') , store = shared.store || (shared.store = new (__webpack_require__(222))); var getOrCreateMetadataMap = function(target, targetKey, create){ var targetMetadata = store.get(target); if(!targetMetadata){ if(!create)return undefined; store.set(target, targetMetadata = new Map); } var keyMetadata = targetMetadata.get(targetKey); if(!keyMetadata){ if(!create)return undefined; targetMetadata.set(targetKey, keyMetadata = new Map); } return keyMetadata; }; var ordinaryHasOwnMetadata = function(MetadataKey, O, P){ var metadataMap = getOrCreateMetadataMap(O, P, false); return metadataMap === undefined ? false : metadataMap.has(MetadataKey); }; var ordinaryGetOwnMetadata = function(MetadataKey, O, P){ var metadataMap = getOrCreateMetadataMap(O, P, false); return metadataMap === undefined ? undefined : metadataMap.get(MetadataKey); }; var ordinaryDefineOwnMetadata = function(MetadataKey, MetadataValue, O, P){ getOrCreateMetadataMap(O, P, true).set(MetadataKey, MetadataValue); }; var ordinaryOwnMetadataKeys = function(target, targetKey){ var metadataMap = getOrCreateMetadataMap(target, targetKey, false) , keys = []; if(metadataMap)metadataMap.forEach(function(_, key){ keys.push(key); }); return keys; }; var toMetaKey = function(it){ return it === undefined || typeof it == 'symbol' ? it : String(it); }; var exp = function(O){ $export($export.S, 'Reflect', O); }; module.exports = { store: store, map: getOrCreateMetadataMap, has: ordinaryHasOwnMetadata, get: ordinaryGetOwnMetadata, set: ordinaryDefineOwnMetadata, keys: ordinaryOwnMetadataKeys, key: toMetaKey, exp: exp }; /***/ }), /* 285 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , toMetaKey = metadata.key , getOrCreateMetadataMap = metadata.map , store = metadata.store; metadata.exp({deleteMetadata: function deleteMetadata(metadataKey, target /*, targetKey */){ var targetKey = arguments.length < 3 ? undefined : toMetaKey(arguments[2]) , metadataMap = getOrCreateMetadataMap(anObject(target), targetKey, false); if(metadataMap === undefined || !metadataMap['delete'](metadataKey))return false; if(metadataMap.size)return true; var targetMetadata = store.get(target); targetMetadata['delete'](targetKey); return !!targetMetadata.size || store['delete'](target); }}); /***/ }), /* 286 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , getPrototypeOf = __webpack_require__(66) , ordinaryHasOwnMetadata = metadata.has , ordinaryGetOwnMetadata = metadata.get , toMetaKey = metadata.key; var ordinaryGetMetadata = function(MetadataKey, O, P){ var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P); if(hasOwn)return ordinaryGetOwnMetadata(MetadataKey, O, P); var parent = getPrototypeOf(O); return parent !== null ? ordinaryGetMetadata(MetadataKey, parent, P) : undefined; }; metadata.exp({getMetadata: function getMetadata(metadataKey, target /*, targetKey */){ return ordinaryGetMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2])); }}); /***/ }), /* 287 */ /***/ (function(module, exports, __webpack_require__) { var Set = __webpack_require__(221) , from = __webpack_require__(275) , metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , getPrototypeOf = __webpack_require__(66) , ordinaryOwnMetadataKeys = metadata.keys , toMetaKey = metadata.key; var ordinaryMetadataKeys = function(O, P){ var oKeys = ordinaryOwnMetadataKeys(O, P) , parent = getPrototypeOf(O); if(parent === null)return oKeys; var pKeys = ordinaryMetadataKeys(parent, P); return pKeys.length ? oKeys.length ? from(new Set(oKeys.concat(pKeys))) : pKeys : oKeys; }; metadata.exp({getMetadataKeys: function getMetadataKeys(target /*, targetKey */){ return ordinaryMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1])); }}); /***/ }), /* 288 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , ordinaryGetOwnMetadata = metadata.get , toMetaKey = metadata.key; metadata.exp({getOwnMetadata: function getOwnMetadata(metadataKey, target /*, targetKey */){ return ordinaryGetOwnMetadata(metadataKey, anObject(target) , arguments.length < 3 ? undefined : toMetaKey(arguments[2])); }}); /***/ }), /* 289 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , ordinaryOwnMetadataKeys = metadata.keys , toMetaKey = metadata.key; metadata.exp({getOwnMetadataKeys: function getOwnMetadataKeys(target /*, targetKey */){ return ordinaryOwnMetadataKeys(anObject(target), arguments.length < 2 ? undefined : toMetaKey(arguments[1])); }}); /***/ }), /* 290 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , getPrototypeOf = __webpack_require__(66) , ordinaryHasOwnMetadata = metadata.has , toMetaKey = metadata.key; var ordinaryHasMetadata = function(MetadataKey, O, P){ var hasOwn = ordinaryHasOwnMetadata(MetadataKey, O, P); if(hasOwn)return true; var parent = getPrototypeOf(O); return parent !== null ? ordinaryHasMetadata(MetadataKey, parent, P) : false; }; metadata.exp({hasMetadata: function hasMetadata(metadataKey, target /*, targetKey */){ return ordinaryHasMetadata(metadataKey, anObject(target), arguments.length < 3 ? undefined : toMetaKey(arguments[2])); }}); /***/ }), /* 291 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , ordinaryHasOwnMetadata = metadata.has , toMetaKey = metadata.key; metadata.exp({hasOwnMetadata: function hasOwnMetadata(metadataKey, target /*, targetKey */){ return ordinaryHasOwnMetadata(metadataKey, anObject(target) , arguments.length < 3 ? undefined : toMetaKey(arguments[2])); }}); /***/ }), /* 292 */ /***/ (function(module, exports, __webpack_require__) { var metadata = __webpack_require__(284) , anObject = __webpack_require__(19) , aFunction = __webpack_require__(28) , toMetaKey = metadata.key , ordinaryDefineOwnMetadata = metadata.set; metadata.exp({metadata: function metadata(metadataKey, metadataValue){ return function decorator(target, targetKey){ ordinaryDefineOwnMetadata( metadataKey, metadataValue, (targetKey !== undefined ? anObject : aFunction)(target), toMetaKey(targetKey) ); }; }}); /***/ }), /* 293 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/rwaldron/tc39-notes/blob/master/es6/2014-09/sept-25.md#510-globalasap-for-enqueuing-a-microtask var $export = __webpack_require__(15) , microtask = __webpack_require__(216)() , process = __webpack_require__(11).process , isNode = __webpack_require__(41)(process) == 'process'; $export($export.G, { asap: function asap(fn){ var domain = isNode && process.domain; microtask(domain ? domain.bind(fn) : fn); } }); /***/ }), /* 294 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; // https://github.com/zenparsing/es-observable var $export = __webpack_require__(15) , global = __webpack_require__(11) , core = __webpack_require__(16) , microtask = __webpack_require__(216)() , OBSERVABLE = __webpack_require__(32)('observable') , aFunction = __webpack_require__(28) , anObject = __webpack_require__(19) , anInstance = __webpack_require__(212) , redefineAll = __webpack_require__(217) , hide = __webpack_require__(17) , forOf = __webpack_require__(213) , RETURN = forOf.RETURN; var getMethod = function(fn){ return fn == null ? undefined : aFunction(fn); }; var cleanupSubscription = function(subscription){ var cleanup = subscription._c; if(cleanup){ subscription._c = undefined; cleanup(); } }; var subscriptionClosed = function(subscription){ return subscription._o === undefined; }; var closeSubscription = function(subscription){ if(!subscriptionClosed(subscription)){ subscription._o = undefined; cleanupSubscription(subscription); } }; var Subscription = function(observer, subscriber){ anObject(observer); this._c = undefined; this._o = observer; observer = new SubscriptionObserver(this); try { var cleanup = subscriber(observer) , subscription = cleanup; if(cleanup != null){ if(typeof cleanup.unsubscribe === 'function')cleanup = function(){ subscription.unsubscribe(); }; else aFunction(cleanup); this._c = cleanup; } } catch(e){ observer.error(e); return; } if(subscriptionClosed(this))cleanupSubscription(this); }; Subscription.prototype = redefineAll({}, { unsubscribe: function unsubscribe(){ closeSubscription(this); } }); var SubscriptionObserver = function(subscription){ this._s = subscription; }; SubscriptionObserver.prototype = redefineAll({}, { next: function next(value){ var subscription = this._s; if(!subscriptionClosed(subscription)){ var observer = subscription._o; try { var m = getMethod(observer.next); if(m)return m.call(observer, value); } catch(e){ try { closeSubscription(subscription); } finally { throw e; } } } }, error: function error(value){ var subscription = this._s; if(subscriptionClosed(subscription))throw value; var observer = subscription._o; subscription._o = undefined; try { var m = getMethod(observer.error); if(!m)throw value; value = m.call(observer, value); } catch(e){ try { cleanupSubscription(subscription); } finally { throw e; } } cleanupSubscription(subscription); return value; }, complete: function complete(value){ var subscription = this._s; if(!subscriptionClosed(subscription)){ var observer = subscription._o; subscription._o = undefined; try { var m = getMethod(observer.complete); value = m ? m.call(observer, value) : undefined; } catch(e){ try { cleanupSubscription(subscription); } finally { throw e; } } cleanupSubscription(subscription); return value; } } }); var $Observable = function Observable(subscriber){ anInstance(this, $Observable, 'Observable', '_f')._f = aFunction(subscriber); }; redefineAll($Observable.prototype, { subscribe: function subscribe(observer){ return new Subscription(observer, this._f); }, forEach: function forEach(fn){ var that = this; return new (core.Promise || global.Promise)(function(resolve, reject){ aFunction(fn); var subscription = that.subscribe({ next : function(value){ try { return fn(value); } catch(e){ reject(e); subscription.unsubscribe(); } }, error: reject, complete: resolve }); }); } }); redefineAll($Observable, { from: function from(x){ var C = typeof this === 'function' ? this : $Observable; var method = getMethod(anObject(x)[OBSERVABLE]); if(method){ var observable = anObject(method.call(x)); return observable.constructor === C ? observable : new C(function(observer){ return observable.subscribe(observer); }); } return new C(function(observer){ var done = false; microtask(function(){ if(!done){ try { if(forOf(x, false, function(it){ observer.next(it); if(done)return RETURN; }) === RETURN)return; } catch(e){ if(done)throw e; observer.error(e); return; } observer.complete(); } }); return function(){ done = true; }; }); }, of: function of(){ for(var i = 0, l = arguments.length, items = Array(l); i < l;)items[i] = arguments[i++]; return new (typeof this === 'function' ? this : $Observable)(function(observer){ var done = false; microtask(function(){ if(!done){ for(var i = 0; i < items.length; ++i){ observer.next(items[i]); if(done)return; } observer.complete(); } }); return function(){ done = true; }; }); } }); hide($Observable.prototype, OBSERVABLE, function(){ return this; }); $export($export.G, {Observable: $Observable}); __webpack_require__(199)('Observable'); /***/ }), /* 295 */ /***/ (function(module, exports, __webpack_require__) { // ie9- setTimeout & setInterval additional parameters fix var global = __webpack_require__(11) , $export = __webpack_require__(15) , invoke = __webpack_require__(85) , partial = __webpack_require__(296) , navigator = global.navigator , MSIE = !!navigator && /MSIE .\./.test(navigator.userAgent); // <- dirty ie9- check var wrap = function(set){ return MSIE ? function(fn, time /*, ...args */){ return set(invoke( partial, [].slice.call(arguments, 2), typeof fn == 'function' ? fn : Function(fn) ), time); } : set; }; $export($export.G + $export.B + $export.F * MSIE, { setTimeout: wrap(global.setTimeout), setInterval: wrap(global.setInterval) }); /***/ }), /* 296 */ /***/ (function(module, exports, __webpack_require__) { 'use strict'; var path = __webpack_require__(297) , invoke = __webpack_require__(85) , aFunction = __webpack_require__(28); module.exports = function(/* ...pargs */){ var fn = aFunction(this) , length = arguments.length , pargs = Array(length) , i = 0 , _ = path._ , holder = false; while(length > i)if((pargs[i] = arguments[i++]) === _)holder = true; return function(/* ...args */){ var that = this , aLen = arguments.length , j = 0, k = 0, args; if(!holder && !aLen)return invoke(fn, pargs, that); args = pargs.slice(); if(holder)for(;length > j; j++)if(args[j] === _)args[j] = arguments[k++]; while(aLen > k)args.push(arguments[k++]); return invoke(fn, args, that); }; }; /***/ }), /* 297 */ /***/ (function(module, exports, __webpack_require__) { module.exports = __webpack_require__(11); /***/ }), /* 298 */ /***/ (function(module, exports, __webpack_require__) { var $export = __webpack_require__(15) , $task = __webpack_require__(215); $export($export.G + $export.B, { setImmediate: $task.set, clearImmediate: $task.clear }); /***/ }), /* 299 */ /***/ (function(module, exports, __webpack_require__) { var $iterators = __webpack_require__(200) , redefine = __webpack_require__(25) , global = __webpack_require__(11) , hide = __webpack_require__(17) , Iterators = __webpack_require__(136) , wks = __webpack_require__(32) , ITERATOR = wks('iterator') , TO_STRING_TAG = wks('toStringTag') , ArrayValues = Iterators.Array; for(var collections = ['NodeList', 'DOMTokenList', 'MediaList', 'StyleSheetList', 'CSSRuleList'], i = 0; i < 5; i++){ var NAME = collections[i] , Collection = global[NAME] , proto = Collection && Collection.prototype , key; if(proto){ if(!proto[ITERATOR])hide(proto, ITERATOR, ArrayValues); if(!proto[TO_STRING_TAG])hide(proto, TO_STRING_TAG, NAME); Iterators[NAME] = ArrayValues; for(key in $iterators)if(!proto[key])redefine(proto, key, $iterators[key], true); } } /***/ }), /* 300 */ /***/ (function(module, exports) { /* WEBPACK VAR INJECTION */(function(global) {/** * Copyright (c) 2014, Facebook, Inc. * All rights reserved. * * This source code is licensed under the BSD-style license found in the * https://raw.github.com/facebook/regenerator/master/LICENSE file. An * additional grant of patent rights can be found in the PATENTS file in * the same directory. */ !(function(global) { "use strict"; var Op = Object.prototype; var hasOwn = Op.hasOwnProperty; var undefined; // More compressible than void 0. var $Symbol = typeof Symbol === "function" ? Symbol : {}; var iteratorSymbol = $Symbol.iterator || "@@iterator"; var asyncIteratorSymbol = $Symbol.asyncIterator || "@@asyncIterator"; var toStringTagSymbol = $Symbol.toStringTag || "@@toStringTag"; var inModule = typeof module === "object"; var runtime = global.regeneratorRuntime; if (runtime) { if (inModule) { // If regeneratorRuntime is defined globally and we're in a module, // make the exports object identical to regeneratorRuntime. module.exports = runtime; } // Don't bother evaluating the rest of this file if the runtime was // already defined globally. return; } // Define the runtime globally (as expected by generated code) as either // module.exports (if we're in a module) or a new, empty object. runtime = global.regeneratorRuntime = inModule ? module.exports : {}; function wrap(innerFn, outerFn, self, tryLocsList) { // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator. var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator; var generator = Object.create(protoGenerator.prototype); var context = new Context(tryLocsList || []); // The ._invoke method unifies the implementations of the .next, // .throw, and .return methods. generator._invoke = makeInvokeMethod(innerFn, self, context); return generator; } runtime.wrap = wrap; // Try/catch helper to minimize deoptimizations. Returns a completion // record like context.tryEntries[i].completion. This interface could // have been (and was previously) designed to take a closure to be // invoked without arguments, but in all the cases we care about we // already have an existing method we want to call, so there's no need // to create a new function object. We can even get away with assuming // the method takes exactly one argument, since that happens to be true // in every case, so we don't have to touch the arguments object. The // only additional allocation required is the completion record, which // has a stable shape and so hopefully should be cheap to allocate. function tryCatch(fn, obj, arg) { try { return { type: "normal", arg: fn.call(obj, arg) }; } catch (err) { return { type: "throw", arg: err }; } } var GenStateSuspendedStart = "suspendedStart"; var GenStateSuspendedYield = "suspendedYield"; var GenStateExecuting = "executing"; var GenStateCompleted = "completed"; // Returning this object from the innerFn has the same effect as // breaking out of the dispatch switch statement. var ContinueSentinel = {}; // Dummy constructor functions that we use as the .constructor and // .constructor.prototype properties for functions that return Generator // objects. For full spec compliance, you may wish to configure your // minifier not to mangle the names of these two functions. function Generator() {} function GeneratorFunction() {} function GeneratorFunctionPrototype() {} // This is a polyfill for %IteratorPrototype% for environments that // don't natively support it. var IteratorPrototype = {}; IteratorPrototype[iteratorSymbol] = function () { return this; }; var getProto = Object.getPrototypeOf; var NativeIteratorPrototype = getProto && getProto(getProto(values([]))); if (NativeIteratorPrototype && NativeIteratorPrototype !== Op && hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) { // This environment has a native %IteratorPrototype%; use it instead // of the polyfill. IteratorPrototype = NativeIteratorPrototype; } var Gp = GeneratorFunctionPrototype.prototype = Generator.prototype = Object.create(IteratorPrototype); GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype; GeneratorFunctionPrototype.constructor = GeneratorFunction; GeneratorFunctionPrototype[toStringTagSymbol] = GeneratorFunction.displayName = "GeneratorFunction"; // Helper for defining the .next, .throw, and .return methods of the // Iterator interface in terms of a single ._invoke method. function defineIteratorMethods(prototype) { ["next", "throw", "return"].forEach(function(method) { prototype[method] = function(arg) { return this._invoke(method, arg); }; }); } runtime.isGeneratorFunction = function(genFun) { var ctor = typeof genFun === "function" && genFun.constructor; return ctor ? ctor === GeneratorFunction || // For the native GeneratorFunction constructor, the best we can // do is to check its .name property. (ctor.displayName || ctor.name) === "GeneratorFunction" : false; }; runtime.mark = function(genFun) { if (Object.setPrototypeOf) { Object.setPrototypeOf(genFun, GeneratorFunctionPrototype); } else { genFun.__proto__ = GeneratorFunctionPrototype; if (!(toStringTagSymbol in genFun)) { genFun[toStringTagSymbol] = "GeneratorFunction"; } } genFun.prototype = Object.create(Gp); return genFun; }; // Within the body of any async function, `await x` is transformed to // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test // `hasOwn.call(value, "__await")` to determine if the yielded value is // meant to be awaited. runtime.awrap = function(arg) { return { __await: arg }; }; function AsyncIterator(generator) { function invoke(method, arg, resolve, reject) { var record = tryCatch(generator[method], generator, arg); if (record.type === "throw") { reject(record.arg); } else { var result = record.arg; var value = result.value; if (value && typeof value === "object" && hasOwn.call(value, "__await")) { return Promise.resolve(value.__await).then(function(value) { invoke("next", value, resolve, reject); }, function(err) { invoke("throw", err, resolve, reject); }); } return Promise.resolve(value).then(function(unwrapped) { // When a yielded Promise is resolved, its final value becomes // the .value of the Promise<{value,done}> result for the // current iteration. If the Promise is rejected, however, the // result for this iteration will be rejected with the same // reason. Note that rejections of yielded Promises are not // thrown back into the generator function, as is the case // when an awaited Promise is rejected. This difference in // behavior between yield and await is important, because it // allows the consumer to decide what to do with the yielded // rejection (swallow it and continue, manually .throw it back // into the generator, abandon iteration, whatever). With // await, by contrast, there is no opportunity to examine the // rejection reason outside the generator function, so the // only option is to throw it from the await expression, and // let the generator function handle the exception. result.value = unwrapped; resolve(result); }, reject); } } if (typeof global.process === "object" && global.process.domain) { invoke = global.process.domain.bind(invoke); } var previousPromise; function enqueue(method, arg) { function callInvokeWithMethodAndArg() { return new Promise(function(resolve, reject) { invoke(method, arg, resolve, reject); }); } return previousPromise = // If enqueue has been called before, then we want to wait until // all previous Promises have been resolved before calling invoke, // so that results are always delivered in the correct order. If // enqueue has not been called before, then it is important to // call invoke immediately, without waiting on a callback to fire, // so that the async generator function has the opportunity to do // any necessary setup in a predictable way. This predictability // is why the Promise constructor synchronously invokes its // executor callback, and why async functions synchronously // execute code before the first await. Since we implement simple // async functions in terms of async generators, it is especially // important to get this right, even though it requires care. previousPromise ? previousPromise.then( callInvokeWithMethodAndArg, // Avoid propagating failures to Promises returned by later // invocations of the iterator. callInvokeWithMethodAndArg ) : callInvokeWithMethodAndArg(); } // Define the unified helper method that is used to implement .next, // .throw, and .return (see defineIteratorMethods). this._invoke = enqueue; } defineIteratorMethods(AsyncIterator.prototype); AsyncIterator.prototype[asyncIteratorSymbol] = function () { return this; }; runtime.AsyncIterator = AsyncIterator; // Note that simple async functions are implemented on top of // AsyncIterator objects; they just return a Promise for the value of // the final result produced by the iterator. runtime.async = function(innerFn, outerFn, self, tryLocsList) { var iter = new AsyncIterator( wrap(innerFn, outerFn, self, tryLocsList) ); return runtime.isGeneratorFunction(outerFn) ? iter // If outerFn is a generator, return the full iterator. : iter.next().then(function(result) { return result.done ? result.value : iter.next(); }); }; function makeInvokeMethod(innerFn, self, context) { var state = GenStateSuspendedStart; return function invoke(method, arg) { if (state === GenStateExecuting) { throw new Error("Generator is already running"); } if (state === GenStateCompleted) { if (method === "throw") { throw arg; } // Be forgiving, per 25.3.3.3.3 of the spec: // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume return doneResult(); } context.method = method; context.arg = arg; while (true) { var delegate = context.delegate; if (delegate) { var delegateResult = maybeInvokeDelegate(delegate, context); if (delegateResult) { if (delegateResult === ContinueSentinel) continue; return delegateResult; } } if (context.method === "next") { // Setting context._sent for legacy support of Babel's // function.sent implementation. context.sent = context._sent = context.arg; } else if (context.method === "throw") { if (state === GenStateSuspendedStart) { state = GenStateCompleted; throw context.arg; } context.dispatchException(context.arg); } else if (context.method === "return") { context.abrupt("return", context.arg); } state = GenStateExecuting; var record = tryCatch(innerFn, self, context); if (record.type === "normal") { // If an exception is thrown from innerFn, we leave state === // GenStateExecuting and loop back for another invocation. state = context.done ? GenStateCompleted : GenStateSuspendedYield; if (record.arg === ContinueSentinel) { continue; } return { value: record.arg, done: context.done }; } else if (record.type === "throw") { state = GenStateCompleted; // Dispatch the exception by looping back around to the // context.dispatchException(context.arg) call above. context.method = "throw"; context.arg = record.arg; } } }; } // Call delegate.iterator[context.method](context.arg) and handle the // result, either by returning a { value, done } result from the // delegate iterator, or by modifying context.method and context.arg, // setting context.delegate to null, and returning the ContinueSentinel. function maybeInvokeDelegate(delegate, context) { var method = delegate.iterator[context.method]; if (method === undefined) { // A .throw or .return when the delegate iterator has no .throw // method always terminates the yield* loop. context.delegate = null; if (context.method === "throw") { if (delegate.iterator.return) { // If the delegate iterator has a return method, give it a // chance to clean up. context.method = "return"; context.arg = undefined; maybeInvokeDelegate(delegate, context); if (context.method === "throw") { // If maybeInvokeDelegate(context) changed context.method from // "return" to "throw", let that override the TypeError below. return ContinueSentinel; } } context.method = "throw"; context.arg = new TypeError( "The iterator does not provide a 'throw' method"); } return ContinueSentinel; } var record = tryCatch(method, delegate.iterator, context.arg); if (record.type === "throw") { context.method = "throw"; context.arg = record.arg; context.delegate = null; return ContinueSentinel; } var info = record.arg; if (! info) { context.method = "throw"; context.arg = new TypeError("iterator result is not an object"); context.delegate = null; return ContinueSentinel; } if (info.done) { // Assign the result of the finished delegate to the temporary // variable specified by delegate.resultName (see delegateYield). context[delegate.resultName] = info.value; // Resume execution at the desired location (see delegateYield). context.next = delegate.nextLoc; // If context.method was "throw" but the delegate handled the // exception, let the outer generator proceed normally. If // context.method was "next", forget context.arg since it has been // "consumed" by the delegate iterator. If context.method was // "return", allow the original .return call to continue in the // outer generator. if (context.method !== "return") { context.method = "next"; context.arg = undefined; } } else { // Re-yield the result returned by the delegate method. return info; } // The delegate iterator is finished, so forget it and continue with // the outer generator. context.delegate = null; return ContinueSentinel; } // Define Generator.prototype.{next,throw,return} in terms of the // unified ._invoke helper method. defineIteratorMethods(Gp); Gp[toStringTagSymbol] = "Generator"; // A Generator should always return itself as the iterator object when the // @@iterator function is called on it. Some browsers' implementations of the // iterator prototype chain incorrectly implement this, causing the Generator // object to not be returned from this call. This ensures that doesn't happen. // See https://github.com/facebook/regenerator/issues/274 for more details. Gp[iteratorSymbol] = function() { return this; }; Gp.toString = function() { return "[object Generator]"; }; function pushTryEntry(locs) { var entry = { tryLoc: locs[0] }; if (1 in locs) { entry.catchLoc = locs[1]; } if (2 in locs) { entry.finallyLoc = locs[2]; entry.afterLoc = locs[3]; } this.tryEntries.push(entry); } function resetTryEntry(entry) { var record = entry.completion || {}; record.type = "normal"; delete record.arg; entry.completion = record; } function Context(tryLocsList) { // The root entry object (effectively a try statement without a catch // or a finally block) gives us a place to store values thrown from // locations where there is no enclosing try statement. this.tryEntries = [{ tryLoc: "root" }]; tryLocsList.forEach(pushTryEntry, this); this.reset(true); } runtime.keys = function(object) { var keys = []; for (var key in object) { keys.push(key); } keys.reverse(); // Rather than returning an object with a next method, we keep // things simple and return the next function itself. return function next() { while (keys.length) { var key = keys.pop(); if (key in object) { next.value = key; next.done = false; return next; } } // To avoid creating an additional object, we just hang the .value // and .done properties off the next function object itself. This // also ensures that the minifier will not anonymize the function. next.done = true; return next; }; }; function values(iterable) { if (iterable) { var iteratorMethod = iterable[iteratorSymbol]; if (iteratorMethod) { return iteratorMethod.call(iterable); } if (typeof iterable.next === "function") { return iterable; } if (!isNaN(iterable.length)) { var i = -1, next = function next() { while (++i < iterable.length) { if (hasOwn.call(iterable, i)) { next.value = iterable[i]; next.done = false; return next; } } next.value = undefined; next.done = true; return next; }; return next.next = next; } } // Return an iterator with no values. return { next: doneResult }; } runtime.values = values; function doneResult() { return { value: undefined, done: true }; } Context.prototype = { constructor: Context, reset: function(skipTempReset) { this.prev = 0; this.next = 0; // Resetting context._sent for legacy support of Babel's // function.sent implementation. this.sent = this._sent = undefined; this.done = false; this.delegate = null; this.method = "next"; this.arg = undefined; this.tryEntries.forEach(resetTryEntry); if (!skipTempReset) { for (var name in this) { // Not sure about the optimal order of these conditions: if (name.charAt(0) === "t" && hasOwn.call(this, name) && !isNaN(+name.slice(1))) { this[name] = undefined; } } } }, stop: function() { this.done = true; var rootEntry = this.tryEntries[0]; var rootRecord = rootEntry.completion; if (rootRecord.type === "throw") { throw rootRecord.arg; } return this.rval; }, dispatchException: function(exception) { if (this.done) { throw exception; } var context = this; function handle(loc, caught) { record.type = "throw"; record.arg = exception; context.next = loc; if (caught) { // If the dispatched exception was caught by a catch block, // then let that catch block handle the exception normally. context.method = "next"; context.arg = undefined; } return !! caught; } for (var i = this.tryEntries.length - 1; i >= 0; --i) { var entry = this.tryEntries[i]; var record = entry.completion; if (entry.tryLoc === "root") { // Exception thrown outside of any try block that could handle // it, so set the completion value of the entire function to // throw the exception. return handle("end"); } if (entry.tryLoc <= this.prev) { var hasCatch = hasOwn.call(entry, "catchLoc"); var hasFinally = hasOwn.call(entry, "finallyLoc"); if (hasCatch && hasFinally) { if (this.prev < entry.catchLoc) { return handle(entry.catchLoc, true); } else if (this.prev < entry.finallyLoc) { return handle(entry.finallyLoc); } } else if (hasCatch) { if (this.prev < entry.catchLoc) { return handle(entry.catchLoc, true); } } else if (hasFinally) { if (this.prev < entry.finallyLoc) { return handle(entry.finallyLoc); } } else { throw new Error("try statement without catch or finally"); } } } }, abrupt: function(type, arg) { for (var i = this.tryEntries.length - 1; i >= 0; --i) { var entry = this.tryEntries[i]; if (entry.tryLoc <= this.prev && hasOwn.call(entry, "finallyLoc") && this.prev < entry.finallyLoc) { var finallyEntry = entry; break; } } if (finallyEntry && (type === "break" || type === "continue") && finallyEntry.tryLoc <= arg && arg <= finallyEntry.finallyLoc) { // Ignore the finally entry if control is not jumping to a // location outside the try/catch block. finallyEntry = null; } var record = finallyEntry ? finallyEntry.completion : {}; record.type = type; record.arg = arg; if (finallyEntry) { this.method = "next"; this.next = finallyEntry.finallyLoc; return ContinueSentinel; } return this.complete(record); }, complete: function(record, afterLoc) { if (record.type === "throw") { throw record.arg; } if (record.type === "break" || record.type === "continue") { this.next = record.arg; } else if (record.type === "return") { this.rval = this.arg = record.arg; this.method = "return"; this.next = "end"; } else if (record.type === "normal" && afterLoc) { this.next = afterLoc; } return ContinueSentinel; }, finish: function(finallyLoc) { for (var i = this.tryEntries.length - 1; i >= 0; --i) { var entry = this.tryEntries[i]; if (entry.finallyLoc === finallyLoc) { this.complete(entry.completion, entry.afterLoc); resetTryEntry(entry); return ContinueSentinel; } } }, "catch": function(tryLoc) { for (var i = this.tryEntries.length - 1; i >= 0; --i) { var entry = this.tryEntries[i]; if (entry.tryLoc === tryLoc) { var record = entry.completion; if (record.type === "throw") { var thrown = record.arg; resetTryEntry(entry); } return thrown; } } // The context.catch method must only be called with a location // argument that corresponds to a known catch block. throw new Error("illegal catch attempt"); }, delegateYield: function(iterable, resultName, nextLoc) { this.delegate = { iterator: values(iterable), resultName: resultName, nextLoc: nextLoc }; if (this.method === "next") { // Deliberately forget the last sent value so that we don't // accidentally pass it on to the delegate. this.arg = undefined; } return ContinueSentinel; } }; })( // Among the various tricks for obtaining a reference to the global // object, this seems to be the most reliable technique that does not // use indirect eval (which violates Content Security Policy). typeof global === "object" ? global : typeof window === "object" ? window : typeof self === "object" ? self : this ); /* WEBPACK VAR INJECTION */}.call(exports, (function() { return this; }()))) /***/ }), /* 301 */ /***/ (function(module, exports, __webpack_require__) { __webpack_require__(302); module.exports = __webpack_require__(16).RegExp.escape; /***/ }), /* 302 */ /***/ (function(module, exports, __webpack_require__) { // https://github.com/benjamingr/RexExp.escape var $export = __webpack_require__(15) , $re = __webpack_require__(303)(/[\\^$*+?.()|[\]{}]/g, '\\$&'); $export($export.S, 'RegExp', {escape: function escape(it){ return $re(it); }}); /***/ }), /* 303 */ /***/ (function(module, exports) { module.exports = function(regExp, replace){ var replacer = replace === Object(replace) ? function(part){ return replace[part]; } : replace; return function(it){ return String(it).replace(regExp, replacer); }; }; /***/ }), /* 304 */ /***/ (function(module, exports, __webpack_require__) { // style-loader: Adds some css to the DOM by adding a <style> tag // load the styles var content = __webpack_require__(305); if(typeof content === 'string') content = [[module.id, content, '']]; // add the styles to the DOM var update = __webpack_require__(307)(content, {}); if(content.locals) module.exports = content.locals; // Hot Module Replacement if(false) { // When the styles change, update the <style> tags if(!content.locals) { module.hot.accept("!!./node_modules/css-loader/index.js!./style.css", function() { var newContent = require("!!./node_modules/css-loader/index.js!./style.css"); if(typeof newContent === 'string') newContent = [[module.id, newContent, '']]; update(newContent); }); } // When the module is disposed, remove the <style> tags module.hot.dispose(function() { update(); }); } /***/ }), /* 305 */ /***/ (function(module, exports, __webpack_require__) { exports = module.exports = __webpack_require__(306)(); // imports // module exports.push([module.id, ".lime {\n all: initial;\n}\n.lime.top_div {\n display: flex;\n flex-wrap: wrap;\n}\n.lime.predict_proba {\n width: 245px;\n}\n.lime.predicted_value {\n width: 245px;\n}\n.lime.explanation {\n width: 350px;\n}\n\n.lime.text_div {\n max-height:300px;\n flex: 1 0 300px;\n overflow:scroll;\n}\n.lime.table_div {\n max-height:300px;\n flex: 1 0 300px;\n overflow:scroll;\n}\n.lime.table_div table {\n border-collapse: collapse;\n color: white;\n border-style: hidden;\n margin: 0 auto;\n}\n", ""]); // exports /***/ }), /* 306 */ /***/ (function(module, exports) { /* MIT License http://www.opensource.org/licenses/mit-license.php Author Tobias Koppers @sokra */ // css base code, injected by the css-loader module.exports = function() { var list = []; // return the list of modules as css string list.toString = function toString() { var result = []; for(var i = 0; i < this.length; i++) { var item = this[i]; if(item[2]) { result.push("@media " + item[2] + "{" + item[1] + "}"); } else { result.push(item[1]); } } return result.join(""); }; // import a list of modules into the list list.i = function(modules, mediaQuery) { if(typeof modules === "string") modules = [[null, modules, ""]]; var alreadyImportedModules = {}; for(var i = 0; i < this.length; i++) { var id = this[i][0]; if(typeof id === "number") alreadyImportedModules[id] = true; } for(i = 0; i < modules.length; i++) { var item = modules[i]; // skip already imported module // this implementation is not 100% perfect for weird media query combinations // when a module is imported multiple times with different media queries. // I hope this will never occur (Hey this way we have smaller bundles) if(typeof item[0] !== "number" || !alreadyImportedModules[item[0]]) { if(mediaQuery && !item[2]) { item[2] = mediaQuery; } else if(mediaQuery) { item[2] = "(" + item[2] + ") and (" + mediaQuery + ")"; } list.push(item); } } }; return list; }; /***/ }), /* 307 */ /***/ (function(module, exports, __webpack_require__) { /* MIT License http://www.opensource.org/licenses/mit-license.php Author Tobias Koppers @sokra */ var stylesInDom = {}, memoize = function(fn) { var memo; return function () { if (typeof memo === "undefined") memo = fn.apply(this, arguments); return memo; }; }, isOldIE = memoize(function() { return /msie [6-9]\b/.test(self.navigator.userAgent.toLowerCase()); }), getHeadElement = memoize(function () { return document.head || document.getElementsByTagName("head")[0]; }), singletonElement = null, singletonCounter = 0, styleElementsInsertedAtTop = []; module.exports = function(list, options) { if(false) { if(typeof document !== "object") throw new Error("The style-loader cannot be used in a non-browser environment"); } options = options || {}; // Force single-tag solution on IE6-9, which has a hard limit on the # of <style> // tags it will allow on a page if (typeof options.singleton === "undefined") options.singleton = isOldIE(); // By default, add <style> tags to the bottom of <head>. if (typeof options.insertAt === "undefined") options.insertAt = "bottom"; var styles = listToStyles(list); addStylesToDom(styles, options); return function update(newList) { var mayRemove = []; for(var i = 0; i < styles.length; i++) { var item = styles[i]; var domStyle = stylesInDom[item.id]; domStyle.refs--; mayRemove.push(domStyle); } if(newList) { var newStyles = listToStyles(newList); addStylesToDom(newStyles, options); } for(var i = 0; i < mayRemove.length; i++) { var domStyle = mayRemove[i]; if(domStyle.refs === 0) { for(var j = 0; j < domStyle.parts.length; j++) domStyle.parts[j](); delete stylesInDom[domStyle.id]; } } }; } function addStylesToDom(styles, options) { for(var i = 0; i < styles.length; i++) { var item = styles[i]; var domStyle = stylesInDom[item.id]; if(domStyle) { domStyle.refs++; for(var j = 0; j < domStyle.parts.length; j++) { domStyle.parts[j](item.parts[j]); } for(; j < item.parts.length; j++) { domStyle.parts.push(addStyle(item.parts[j], options)); } } else { var parts = []; for(var j = 0; j < item.parts.length; j++) { parts.push(addStyle(item.parts[j], options)); } stylesInDom[item.id] = {id: item.id, refs: 1, parts: parts}; } } } function listToStyles(list) { var styles = []; var newStyles = {}; for(var i = 0; i < list.length; i++) { var item = list[i]; var id = item[0]; var css = item[1]; var media = item[2]; var sourceMap = item[3]; var part = {css: css, media: media, sourceMap: sourceMap}; if(!newStyles[id]) styles.push(newStyles[id] = {id: id, parts: [part]}); else newStyles[id].parts.push(part); } return styles; } function insertStyleElement(options, styleElement) { var head = getHeadElement(); var lastStyleElementInsertedAtTop = styleElementsInsertedAtTop[styleElementsInsertedAtTop.length - 1]; if (options.insertAt === "top") { if(!lastStyleElementInsertedAtTop) { head.insertBefore(styleElement, head.firstChild); } else if(lastStyleElementInsertedAtTop.nextSibling) { head.insertBefore(styleElement, lastStyleElementInsertedAtTop.nextSibling); } else { head.appendChild(styleElement); } styleElementsInsertedAtTop.push(styleElement); } else if (options.insertAt === "bottom") { head.appendChild(styleElement); } else { throw new Error("Invalid value for parameter 'insertAt'. Must be 'top' or 'bottom'."); } } function removeStyleElement(styleElement) { styleElement.parentNode.removeChild(styleElement); var idx = styleElementsInsertedAtTop.indexOf(styleElement); if(idx >= 0) { styleElementsInsertedAtTop.splice(idx, 1); } } function createStyleElement(options) { var styleElement = document.createElement("style"); styleElement.type = "text/css"; insertStyleElement(options, styleElement); return styleElement; } function createLinkElement(options) { var linkElement = document.createElement("link"); linkElement.rel = "stylesheet"; insertStyleElement(options, linkElement); return linkElement; } function addStyle(obj, options) { var styleElement, update, remove; if (options.singleton) { var styleIndex = singletonCounter++; styleElement = singletonElement || (singletonElement = createStyleElement(options)); update = applyToSingletonTag.bind(null, styleElement, styleIndex, false); remove = applyToSingletonTag.bind(null, styleElement, styleIndex, true); } else if(obj.sourceMap && typeof URL === "function" && typeof URL.createObjectURL === "function" && typeof URL.revokeObjectURL === "function" && typeof Blob === "function" && typeof btoa === "function") { styleElement = createLinkElement(options); update = updateLink.bind(null, styleElement); remove = function() { removeStyleElement(styleElement); if(styleElement.href) URL.revokeObjectURL(styleElement.href); }; } else { styleElement = createStyleElement(options); update = applyToTag.bind(null, styleElement); remove = function() { removeStyleElement(styleElement); }; } update(obj); return function updateStyle(newObj) { if(newObj) { if(newObj.css === obj.css && newObj.media === obj.media && newObj.sourceMap === obj.sourceMap) return; update(obj = newObj); } else { remove(); } }; } var replaceText = (function () { var textStore = []; return function (index, replacement) { textStore[index] = replacement; return textStore.filter(Boolean).join('\n'); }; })(); function applyToSingletonTag(styleElement, index, remove, obj) { var css = remove ? "" : obj.css; if (styleElement.styleSheet) { styleElement.styleSheet.cssText = replaceText(index, css); } else { var cssNode = document.createTextNode(css); var childNodes = styleElement.childNodes; if (childNodes[index]) styleElement.removeChild(childNodes[index]); if (childNodes.length) { styleElement.insertBefore(cssNode, childNodes[index]); } else { styleElement.appendChild(cssNode); } } } function applyToTag(styleElement, obj) { var css = obj.css; var media = obj.media; if(media) { styleElement.setAttribute("media", media) } if(styleElement.styleSheet) { styleElement.styleSheet.cssText = css; } else { while(styleElement.firstChild) { styleElement.removeChild(styleElement.firstChild); } styleElement.appendChild(document.createTextNode(css)); } } function updateLink(linkElement, obj) { var css = obj.css; var sourceMap = obj.sourceMap; if(sourceMap) { // http://stackoverflow.com/a/26603875 css += "\n/*# sourceMappingURL=data:application/json;base64," + btoa(unescape(encodeURIComponent(JSON.stringify(sourceMap)))) + " */"; } var blob = new Blob([css], { type: "text/css" }); var oldSrc = linkElement.href; linkElement.href = URL.createObjectURL(blob); if(oldSrc) URL.revokeObjectURL(oldSrc); } /***/ }) /******/ ]); //# sourceMappingURL=bundle.js.map </script><body> <div class="lime top_div" id="top_divQHZI64S60YHTHRB"></div> <script> var top_div = d3.select('#top_divQHZI64S60YHTHRB').classed('lime top_div', true); var pp_div = top_div.append('div') .classed('lime predicted_value', true); var pp_svg = pp_div.append('svg').style('width', '100%'); var pp = new lime.PredictedValue(pp_svg, 0.39902472498550545, 0.15606073109593918, 0.6681346437797144); var exp_div; var exp = new lime.Explanation(["negative", "positive"]); exp_div = top_div.append('div').classed('lime explanation', true); exp.show([["Wt", 0.09302567838784441], ["Forty", -0.08866808579754581], ["Cone", -0.01380640837834665], ["Shuttle", -0.010305070307180333], ["BenchReps", 0.00874531336563981], ["Vertical", 0.007755906481981503], ["BroadJump", 0.007003560737203991], ["Ht", 0.0007321207284850952]], 1, exp_div); var raw_div = top_div.append('div'); exp.show_raw_tabular([["Wt", "263.00", 0.09302567838784441], ["Forty", "4.81", -0.08866808579754581], ["Cone", "7.22", -0.01380640837834665], ["Shuttle", "4.20", -0.010305070307180333], ["BenchReps", "28.00", 0.00874531336563981], ["Vertical", "32.50", 0.007755906481981503], ["BroadJump", "112.00", 0.007003560737203991], ["Ht", "75.00", 0.0007321207284850952]], 1, raw_div); </script> Great, we have an explanation for that one instance, what about the rest of the test set? We can use the apply method to get explanation for the whole test set. NOTE: We can't parallelize explain_instance with the multiprocessing function since explain_instance is a bound method. In [71]: lime_expl = test_X_imp_df.apply(explainer.explain_instance, predict_fn=estimator.predict, num_features=num_features, axis=1) Before moving on we should double check and see that the local predictions from our surrogate models match our actual predictions. We can judge the local prediction by looking at either the root-mean-squared error or the R2 In [72]: from sklearn.metrics import mean_squared_error, r2_score In [73]: # get all the lime predictions lime_pred = lime_expl.apply(lambda x: x.local_pred[0]) # RMSE of lime pred mean_squared_error(y_pred, lime_pred)**0.5 Out[73]: 0.0539942387861127 In [74]: # r^2 of lime predictions r2_score(y_pred, lime_pred) Out[74]: 0.8255823734122261 If you aren't satisfied with the fit of the local surrogate models you can try to improve them by playing around with the kernel_width parameter in LimeTabularExplainer. We will try and improve the surrogate models by decreasing the kernel width to make the fits more local. In [75]: # new explainer with smaller kernel_width better_explainer = LimeTabularExplainer(train_X_imp_df, mode='regression', feature_names=features, random_state=RANDOM_STATE, discretize_continuous=False, kernel_width=1) In [76]: better_lime_expl = test_X_imp_df.apply(better_explainer.explain_instance, predict_fn=estimator.predict, num_features=num_features, axis=1) In [77]: # get all the lime predictions better_lime_pred = better_lime_expl.apply(lambda x: x.local_pred[0]) # RMSE of lime pred mean_squared_error(y_pred, better_lime_pred)**0.5 Out[77]: 0.036564786949891966 In [78]: # r^2 of lime predictions r2_score(y_pred, better_lime_pred) Out[78]: 0.9200125924240036 Our new local predictions better match our actual predictions. To view all of our explanations at once we can create heatmap in the same manner we did when looking at the feature contributions. To do that we need to create a DataFrame with each instance's feature weights and bias term from the LIME explanation. In [79]: # construct a DataFrame with all the feature weights and bias terms from LIME # create an individual dataframe for each explanation lime_dfs = [pd.DataFrame(dict(expl.as_list() + [('bias', expl.intercept[0])]), index=[0]) for expl in better_lime_expl] # then concatenate them into one big DataFrame lime_expl_df = pd.concat(lime_dfs, ignore_index=True) lime_expl_df.head() Out[79]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } BenchReps BroadJump Cone Forty Ht Shuttle Vertical Wt bias 0 0.010832 0.005010 -0.013976 -0.101409 0.000328 -0.010943 0.006622 0.113631 0.398066 1 0.010999 0.001341 -0.006558 -0.091564 0.004097 -0.006873 0.008604 0.091956 0.417362 2 0.006134 0.006262 -0.014019 -0.105977 -0.001826 -0.013650 0.008932 0.097897 0.398528 3 0.011381 0.016120 -0.016109 -0.109383 -0.001645 -0.012768 0.011177 0.076516 0.387108 4 0.008386 0.002198 -0.012827 -0.052986 0.002043 0.002295 0.003531 0.092838 0.418127 Now that we have the weights for each feature we can measure their prediction contributions by multiplying the weights by the actual feature values. But before we do that we need to scale the data in our test set since LIME scales the data inside the explainer when the data is not discretized. In [80]: # scale the data scaled_X = (test_X_imp_df - explainer.scaler.mean_) / explainer.scaler.scale_ # calc the lime feature contributions lime_feat_contrib = lime_expl_df[features] * scaled_X # add on bias term, actual av %ile and predicted %ile other_lime_cols = ['bias', 'true_AV_pctile', 'pred_AV_pctile'] lime_feat_contrib[other_lime_cols] = pd.DataFrame(np.column_stack((lime_expl_df.bias, y_test_and_pred_df))) lime_feat_contrib.sort_values('pred_AV_pctile', inplace=True) lime_feat_contrib.head() Out[80]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Forty Wt Ht Vertical BenchReps BroadJump Cone Shuttle bias true_AV_pctile pred_AV_pctile 33 -0.100389 -0.082229 0.000335 -0.000075 -0.008814 0.000285 0.000167 0.000342 0.340199 0.002427 0.169148 66 -0.030345 -0.044242 -0.007281 -0.029035 -0.023027 0.000036 -0.037809 -0.014340 0.384037 0.002427 0.178106 62 -0.074079 -0.066658 0.000388 -0.006869 -0.021616 -0.009128 0.000177 0.000684 0.385597 0.002427 0.178733 12 -0.085866 -0.035871 -0.000092 -0.005335 -0.011414 0.000289 0.000165 0.004688 0.394979 0.002427 0.179089 78 -0.087621 -0.090172 -0.000165 -0.000076 -0.002924 0.000320 0.000216 0.000499 0.379018 0.415049 0.180330 Now to plot each set of explanations using our double_heatmap function. Unlike previous heatmaps, we will include the bias terms since the surrogate models that LIME creates can have different bias terms for each player. In [81]: title = 'LIME Feature Contributions for each prediction in the testing data' fig = double_heatmap(lime_feat_contrib[['true_AV_pctile', 'pred_AV_pctile']].T, lime_feat_contrib.loc[:, :'bias'].T, title=title, cbar_label1='%ile', cbar_label2='contribution', subplot_top=0.9) # set the x-axis label for the bottom heatmap # fig has 4 axes object, the first 2 are the heatmaps, the other 2 are the colorbars fig.axes[1].set_xlabel('Player'); SHAP¶SHAP (SHapley Additive exPlanations) is a recent method for model interpretation that leverages game theory to help measure the impact of the features on the predictions. What’s the benefit of using the SHAP method for individual feature contributions over the decision path method from before? Well with the decision path method we have to traverse down the decision tree crediting each feature for the difference in the predictions. This can result in individual contributions that favor features found in splits lower in the tree. The SHAP method doesn't have that problem as it doesn't rely on the order of the features specified by the tree, instead it calculates the contributions by basically averaging the differences in predictions over every possible feature ordering. For more on this topic I suggest reading Scott Lindburg’s (one of the authors of the SHAP paper) blog post. And for a good explanation of how SHAP values are calculated I suggest reading the chapter on SHAP from Christopher Molnar's book. Now lets actually use the SHAP method to explain our predictions. In [82]: import shap # create our SHAP explainer shap_explainer = shap.TreeExplainer(estimator) # calculate the shapley values for our test set test_shap_vals = shap_explainer.shap_values(test_X_imp) The shap package provides us with convenience functions to help us plot the SHAP values for our predictions. We can use force_plot to inspect individual predictions. 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k=this.mainGroup.selectAll(".force-bar-labelBacking").data(b);k.enter().append("path").attr("class","force-bar-labelBacking").attr("stroke","none").attr("opacity",.2).merge(k).attr("d",function(t){return y([[u(t.x)+u(Math.abs(t.effect))+c,23+n],[(t.effect>0?u(t.textx):u(t.textx)+t.textWidth)+c+5,33+n],[(t.effect>0?u(t.textx):u(t.textx)+t.textWidth)+c+5,54+n],[(t.effect>0?u(t.textx)-t.textWidth:u(t.textx))+c-5,54+n],[(t.effect>0?u(t.textx)-t.textWidth:u(t.textx))+c-5,33+n],[u(t.x)+c,23+n]])}).attr("fill",function(t){return"url(#linear-backgrad-"+(t.effect>0?0:1)+")"}),k.exit().remove();var T=this.mainGroup.selectAll(".force-bar-labelDividers").data(b.slice(0,-1));T.enter().append("rect").attr("class","force-bar-labelDividers").attr("height","21px").attr("width","1px").attr("y",33+n).merge(T).attr("x",function(t){return(t.effect>0?u(t.textx):u(t.textx)+t.textWidth)+c+4.5}).attr("fill",function(t){return"url(#linear-grad-"+(t.effect>0?0:1)+")"}),T.exit().remove();var 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null;if("topMouseOut"!==t&&"topMouseOver"!==t)return null;var c;if(u.window===u)c=u;else{var s=u.ownerDocument;c=s?s.defaultView||s.parentWindow:window}var l,f;if("topMouseOut"===t){l=e;var p=n.relatedTarget||n.toElement;f=p?i.getClosestInstanceFromNode(p):null}else l=null,f=e;if(l===f)return null;var h=null==l?c:i.getNodeFromInstance(l),d=null==f?c:i.getNodeFromInstance(f),v=o.getPooled(a.mouseLeave,l,n,u);v.type="mouseleave",v.target=h,v.relatedTarget=d;var g=o.getPooled(a.mouseEnter,f,n,u);return g.type="mouseenter",g.target=d,g.relatedTarget=h,r.accumulateEnterLeaveDispatches(v,g,l,f),[v,g]}};t.exports=u},function(t,e,n){"use strict";var r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){"use 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t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u("109",this.getName()||"ReactCompositeComponent"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u("110"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){"use strict";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(12),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};"undefined"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&"function"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){"use strict";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return" This DOM node was rendered by `"+n+"`."}}return""}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v("137",t._tag,t._currentElement._owner?" 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v=n(2),g=n(3),y=n(332),m=n(334),_=n(20),b=n(81),x=n(21),w=n(156),C=n(22),M=n(82),E=n(51),k=n(157),T=n(4),S=n(351),N=n(352),A=n(158),P=n(355),O=(n(10),n(364)),I=n(369),D=(n(9),n(54)),R=(n(0),n(93),n(79),n(95),n(1),k),L=C.deleteListener,U=T.getNodeFromInstance,F=E.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W="style",V="__html",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:"abort",topCanPlay:"canplay",topCanPlayThrough:"canplaythrough",topDurationChange:"durationchange",topEmptied:"emptied",topEncrypted:"encrypted",topEnded:"ended",topError:"error",topLoadedData:"loadeddata",topLoadedMetadata:"loadedmetadata",topLoadStart:"loadstart",topPause:"pause",topPlay:"play",topPlaying:"playing",topProgress:"progress",topRateChange:"ratechange",topSeeked:"seeked",topSeeking:"seeking",topStalled:"stalled",topSuspend:"suspend",topTimeUpdate:"timeupdate",topVolumeChange:"volumechange",topWaiting:"waiting"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\.\-\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName="ReactDOMComponent",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case"audio":case"form":case"iframe":case"img":case"link":case"object":case"source":case"video":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case"input":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case"option":N.mountWrapper(this,o,e),o=N.getHostProps(this,o);break;case"select":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case"textarea":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&"foreignobject"===f)&&(a=b.html),a===b.html&&("svg"===this._tag?a=b.svg:"math"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if("script"===this._tag){var v=d.createElement("div"),g=this._currentElement.type;v.innerHTML="<"+g+"></"+g+">",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var m=_(h);this._createInitialChildren(t,o,r,m),p=m}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+"/>":x+">"+C+"</"+this._currentElement.type+">"}switch(this._tag){case"input":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case"textarea":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case"select":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case"button":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case"option":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n="<"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=m.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=" "+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=" "+w.createMarkupForRoot()),n+=" "+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r="",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join("")}}return K[this._tag]&&"\n"===r.charAt(0)?"\n"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)""!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case"input":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case"option":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case"select":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case"textarea":o=P.getHostProps(this,o),a=P.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case"input":S.updateWrapper(this);break;case"textarea":P.updateWrapper(this);break;case"select":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]="");this._previousStyleCopy=null}else 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c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart("character",n),i.setEndPoint("EndToStart",i),i.moveEnd("character",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var 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r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],y=a[u];g===y?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(y,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(y)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var 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Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. if (window.IML) IML.ReactDom.render( IML.React.createElement(IML.AdditiveForceVisualizer, {"outNames": ["output value"], "baseValue": 0.42192521813936357, "outValue": 0.4550634394034453, "link": "identity", "featureNames": ["Forty", "Wt", "Ht", "Vertical", "BenchReps", "BroadJump", "Cone", "Shuttle"], "features": {"0": {"effect": 0.030037423841754018, "value": 4.8}, "1": {"effect": -0.002592540037837323, "value": 270.0}, "2": {"effect": -0.0015699007372510024, "value": 76.0}, "3": {"effect": -0.0033978673417343844, "value": 33.0}, "4": {"effect": 0.009918455063014156, "value": 24.0}, "5": {"effect": -0.001433211013861004, "value": 114.0}, "6": {"effect": 0.0007132359339768816, "value": 7.365}, "7": {"effect": 0.0014626255560203985, "value": 4.41}}, "plot_cmap": "RdBu", "labelMargin": 20}), document.getElementById('iIQIM6FIXRTRW1RFG518L') ); In the above explanation we can see that there is a base value (i.e. a bias term) of 0.4219, and that each feature pushes (i.e. adds to) that value in order to reach a final prediction of 0.46. We can also use the force_plot function to look at the explanations for our whole dataset. When using the function in a notebook, it produces an interactive plot that allows you to inspect the SHAP values for each observation by hovering the mouse over the plot. By default the observations are clustered together by how similar they are. For example, the first 17 observations in the plot below are players whose weights have a very large positive impact on their predictions. In [85]: shap.force_plot(test_shap_vals, test_X_imp_df) Out[85]: Visualization omitted, Javascript library not loaded! Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). 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109.0}, "6": {"effect": 0.02125101745050692, "value": 7.07}, "7": {"effect": 0.013460454072935717, "value": 4.26}}}], "plot_cmap": "RdBu"}), document.getElementById('i1SH8A5B2RXFQA6ADSR1R') ); To plot the distribution of each feature's SHAP we can use the summary_plot function. In [86]: shap.summary_plot(test_shap_vals, test_X_imp_df, auto_size_plot=False) With the dependence_plot function we can see how a feature's SHAP values change over the range of feature values. The function automatically colors each point on the plot by a 2nd feature, allowing us to better understand the interaction effects. In [87]: for feat in features: shap.dependence_plot(feat, test_shap_vals, test_X_imp_df, dot_size=100) And of course a heatmap of the SHAP values. In [88]: test_shap_df = pd.DataFrame(np.column_stack((test_shap_vals, y_test_and_pred_df)), columns= features + ['bias', 'true_AV_pctile', 'pred_AV_pctile']) test_shap_df.sort_values('pred_AV_pctile', inplace=True) title = 'SHAP Values for each prediction in the testing data' fig = double_heatmap(test_shap_df[['true_AV_pctile', 'pred_AV_pctile']].T, test_shap_df[features].T, '%ile', 'SHAP Value', title=title, subplot_top=0.89) fig.axes[1].set_xlabel('Player'); Hopefully you found this blog post helpful. If you see any mistakes, have any questions or suggestions [or if you're hiring :)] you can email me at savvas.tjortjoglou@gmail.com, hit me up on Twitter @savvastj, or just leave a comment below. If you like this post and want to support my blog you can check out my patreon page here. Resources¶Here are a list of resources that I found helpful when writing up this post: General Interpretable Machine Learning Feature Importance How are feature_importances in RandomForestClassifier determined? Selecting good features – Part III: random forests Feature Contributions Interpreting random forests Random forest interpretation with scikit-learn Random forest interpretation – conditional feature contributions ICE plots and PDPs Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation LIME "Why Should I Trust You?": Explaining the Predictions of Any Classifier Why your relationship is likely to last (or not): using Local Interpretable Model-Agnostic Explanations (LIME) Understanding LIME SHAP Interpretable Machine Learning with XGBoost Consistent Individualized Feature Attribution for Tree Ensembles NFL Combine/Draft The NFL Combine Actually Matters Part 1 Part 2 Part 3 Videos Machine Learning and Interpretability Towards interpretable reliable models Interpretable Machine Learning Using LIME Framework Explaining behavior of Machine Learning models with eli5 library Model Interpretability Packages treeinterpreter eli5 pycebox pdpbox lime shap Skater As always you can find the notebook and data used for this post on github. <script type="text/javascript">if (!document.getElementById('mathjaxscript_pelican_#%@#$@#')) { var mathjaxscript = document.createElement('script'); mathjaxscript.id = 'mathjaxscript_pelican_#%@#$@#'; mathjaxscript.type = 'text/javascript'; mathjaxscript.src = '//cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'; mathjaxscript[(window.opera ? 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